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Misdiagnoses and medical bias: The gender gap in healthcare
Women are disadvantaged in healthcare – in some cases due to different symptoms in women than in men. This is especially true for heart conditions, where women even face lower survival rates after cardiac arrest.
Canine Science Is Biased Toward the Western World
A new study addresses how studies on domestic dogs tend to focus on a Westernized subset.
Why Hybrid Healthcare Is a Business Imperative for Employers in Nevada
The Nevada Independent is a nonpartisan, nonprofit news and opinion website founded in 2017 by veteran political journalist and commentator Jon Ralston. The site and its supporting channels are focused on ethical, unbiased and transparent journalism. In general, we aim to gather and disseminate important public information and increase civic engagement.
Why Hybrid Healthcare Is a Business Imperative for Employers in Nevada
The Nevada Independent is a nonpartisan, nonprofit news and opinion website founded in 2017 by veteran political journalist and commentator Jon Ralston. The site and its supporting channels are focused on ethical, unbiased and transparent journalism. In general, we aim to gather and disseminate important public information and increase civic engagement.
Trump Lashes Out at National Academies of Sciences Over Climate Guide For Judges
Trump claimed without evidence that the manual was “fraudulent, biased, and misleading.”
Hardship Case At Board Of Health Could Have Broad Implications With…
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Ranking Member Markey, Senator Hirono Reject Trump’s SBA Policy Proposal that Harms Underserved Small Business Owners and Entrepreneurs
Letter (PDF) (Washington, July 17) – Ranking Member Edward J. Markey (D-Mass.) and Senator Mazie Hirono (D-Hawaii) submitted a comment letter to the Small Business Administration (SBA) opposing a proposed rule change by Trump’s SBA to the 8(a) Business Development Program that would harm underserved small business owners and entrepreneurs. The 8(a) Business Development Program was created by Congress more than 50 years ago to level the playing field for small business owners and entrepreneurs who have faced discrimination in starting and operating their businesses. The SBA’s proposed change ignores the existence of historic discrimination that minority and underserved small businesses have faced and continue to face. In the letter, the lawmakers wrote, “SBA’s proposed rule ignores present-day barriers that entrepreneurs from minority and underserved communities face. Discrimination is not dead; minority entrepreneurs continue to face reduced access to contracting opportunities and capital, as well as racial threats and targeting, including unequal treatment, refusal by others to work with minority-owned firms, being called racial slurs, and bias in the workplace. These barriers lead to wide underutilization of, and less revenue for, minority-owned businesses compared to majority-owned businesses, as supported by decades of disparity studies. The proposed rule diminishes the history of systemic racial and ethnic discrimination in the United States by claiming that remedies for discrimination create discrimination in and of themselves.” The lawmakers continued, “The program has always been open to anyone who can prove they have experienced prejudice or cultural bias in education, employment, entrepreneurship, and other facets of our society. Nonetheless SBA has moved forward with the proposed rule. It lacks a reasoned basis, purporting that the federal government has discriminated against White Americans, but offering no substantive qualitative or quantitative data or research in support. If the examples provided in the proposed rule are the only basis for what constitutes discrimination to the agency, they are woefully insufficient. The final rule must provide strong evidence that there is discrimination against White individuals and show how the rule is narrowly tailored to address it. The record remains devoid of evidence of discrimination against White individuals on the basis of race.” Ranking Member Markey is fighting to protect federal programs that address historic discrimination against small business owners from minority and underserved communities, and for a small business agenda that ensures economic opportunity for all. In June, Ranking Member Markey released a statement condemning the SBA’s proposed changes to the 8(a) Business Development Program. Also in June, Ranking Markey released a new report highlighting Trump’s drastic reduction in federal contracting dollars awarded to American small businesses, including minority-owned small businesses. In February, Ranking Member Markey, alongside Senators Hirono and Cory Booker (D-NJ), and Representative Ayanna Pressley (MA-7), introduced the Strengthening Place-based Access, Resources, and Knowledge (SPARK) Act to spur entrepreneurship and increase access to capital and resources for underserved entrepreneurs nationwide. Ranking Member Markey previously held a field hearing in Roxbury, Massachusetts, to discuss how government and the private sector can come together to make entrepreneurship more accessible for underserved communities. Earlier in February, Ranking Member Markey secured funding with the Appropriations Committee for Small Business Development Centers, Women’s Business Centers, SCORE Chapters, and Veterans Business Outreach Centers and provisions to rein in the Administration’s attempts to eliminate programs that support women, veterans, and underserved entrepreneurs. In January, Ranking Member Markey sent a letter to the Consumer Financial Protection Bureau to oppose rule changes that would gut protections against discriminatory lending practices that harm small businesses. In December 2025, Ranking Member Markey sent a letter to SBA Administrator Kelly Loeffler expressing significant concerns with the SBA’s overly burdensome request and unclear guidance for extensive financial documentation over the holidays, as a part of their sweeping attempts to dismantle the 8(a) Business Development Program. In July and December 2025, Ranking Member Markey sent letters to the SBA Administrator demanding answers about the Trump administration’s efforts to limit economic opportunity for American entrepreneurs, including immigrants. In May 2025, Ranking Member Markey sent a letter to the SBA Administrator rejecting President Trump’s FY 2026 budget proposal and calling on SBA to immediately release funding for the entrepreneurial development programs, which include programs like the Women’s Business Centers and the Veteran’s Business Development Centers that support underserved small business owners. In April 2025, Ranking Member Markey introduced the New Start Act of 2025 to establish a five-year pilot program providing grants to support entrepreneurial development training for currently or formerly incarcerated individuals. In February 2025, Ranking Member Markey sent letters to the SBA Administrator and Government Accountability Office (GAO) condemning President Trump’s war on diversity, equity, inclusion, and accessibility (DEIA) policies and asking how the SBA intends to implement Trump’s anti-DEIA executive orders. ###
New Board of Health Members Drop Restrictions On Proposed Woodland…
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Trump admin falsely accuses Smithsonian of 'extreme political activism'
Unbiased Politics News
Mitch McConnell health scare renews questions about transparency in DC
Unbiased Politics News
Tobias Harris explains decision to leave Pistons, sign with Spurs
The Detroit Pistons wanted to retain Tobias Harris in free agency, but ultimately he spurned the Pistons in favor of signing with the San Antonio Spurs on a two-year, $31 million contract. In an exchange with HoopsHype, Harris provided some insight into his decision to leave Detroit for San Antonio. “The urgency to win a championship," Harris said.
Mets recall Joey Gerber, option Tobias Myers to Triple-A
The bullpen churn continues as the Mets look for a fresh arm to add to the mix.
This Week's 10 Must-Read Stories on Women in Politics: Title IX Anniversary, AI's Anti-Women Bias and Ranked-Choice Voting Victories
Weekend Reading on Women’s Representation is a weekly compilation of stories about women’s representation in politics, on boards, in sports and entertainment, in judicial offices and in the private sector in the U.S. and around the world—with a little gardening and goodwill mixed in for refreshment! Published every Friday. —This week marked the 54th anniversary of Title IX. —As the Taliban approaches the five-year mark of its return to power this August, a rare protest in western Afghanistan last week laid bare both the brutality of its rule and the resilience of those living under it. —A woman will replace Eric Swalwell in California … but for not two more months. —A study of 133 AI systems found that 44 percent demonstrated gender bias, and more than a quarter showed both gender and racial bias. ... and more.
Are AI chatbots like ChatGPT politically biased? We tested them.
The Post tested ChatGPT, Gemini and other chatbots with political questions, and the results show that the AI tools have different political leanings.
Tulsi Gabbard influenced by ‘guru’ of ‘cult’ during political career: Report
Unbiased Politics News
Made in Tarrant: Mansfield business executive launches mocktail company
Jacobia Solomon is selling her SipSide Mocktails online or through business partnerships that include hotels and restaurants.
‘The Cardinal Countdown: 78 Days Until Kickoff’
CHARLIE EDGEWORTH #78 Height: 6’7” Weight: 320lb Position: OL Class: Freshman Hometown: Chattanooga, TN Summary: While its not right, and should not be acceptable in our country, I’m out here fighting the good fight and have yet to gain the momentum I need to avoid this blatant hate and bias. I’m of course talking about […]
Merchants of Doubt and the attacks on Extreme Event Attribution science
Last week, I attended a meeting at Columbia University on attribution science and climate law, hosted by the Sabin Center. It was a fantastic event, bringing together scientists and legal experts working at the intersection of extreme event attribution and climate law. Subscribe For those unfamiliar with it, extreme event attribution attempts to quantify the contribution of climate change to an extreme event. For example, several groups analyzed the impact of climate change on Hurricane Harvey’s enormous rainfall totals over Houston, Texas and they found that climate change increased rainfall by 15 to 38%. One thing that came up again and again was how terrified fossil-fuel interests are of extreme event attribution science. They are acutely aware that this research could land them in court. And losing those cases would leave them legally liable for billions of dollars in climate damages. Because the legal stakes are so high, the blowback has turned ugly. I spoke with several scientists at the meeting who are facing ongoing harassment over their work. This blowback is a coordinated campaign to make the entire field look suspect. The goal is to create the impression that attribution science is too uncertain, too political, or too conflicted to be useful in court or in public policy. The strategy is not based on actual science or evidence of misconduct, but on the generation of doubt. The new Merchants of Doubt We’ve seen this before. In fact, not that long ago: We only have to go back a year to the Department of Energy (DOE) Climate Working Group (CWG) report to see an example of using doubt as the tool to push back against well-established science. This strategy is laid out in an email from a member of the CWG, Dr. Roy Spencer, that was released during litigation over the Climate Working Group process. The key quote is: About all I can hope is that what we write will provide sufficient “reasonable scientific doubt” regarding the science claims in the 2009 TSD [technical support document], based upon almost 2 decades of new science, to call into question the original reasoning for the EPA Administrator’s decision that CO2 presents a threat to human health and welfare. This statement is strong evidence that at least some members of the committee were working to support a particular policy outcome: revoking the Endangerment Finding. The email also explains how they planned to do it: by attempting to generate “reasonable doubt”. This is going to be hard, Spencer implies. Despite falsely claiming that “2 decades of new science” weakens the case, Spencer explicitly acknowledges that the actual peer-reviewed science of climate change overwhelmingly rejects his position: But if the science argument is decided upon by a vote, or by the number of published citations, we lose the science argument. We can go back even further: This CWG email shares unmistakable DNA with the infamous 1969 tobacco memo that declared: “Doubt is our product, since it is the best means of competing with the ‘body of fact’ that exists in the mind of the general public. It is also the means of establishing a controversy.” The tobacco memo also acknowledges the limit of this strategy: Like the CWG, they knew the science was not on their side. Share The new new Merchants of Doubt The people attacking the IPCC chapter on extreme event attribution are the newest iteration of the Merchants of Doubt. Their goal, like all Merchants before them, is to introduce doubt into the process. Because the report is not even out yet, they cannot attack its conclusions. So they are attacking the authors instead. Here is a press release from the House Science, Space, and Technology Committee: In the letter, the Chairmen express concerns about potential conflicts of interest involving members of the Attribution Committee, stating that “publicly available information suggests a troubling pattern” in which committee members are affiliated with nonprofits that support climate accountability lawsuits, “raising the appearance of impropriety and member bias.” To be clear, this is just innuendo. There is no actual evidence of bias. And given the robust process that these reports go through, including multiple lines of peer review, it seems very unlikely that significant bias can survive into the report. When the report comes out, critics will have the opportunity to make legitimate criticisms of the report — if any exist. If none do, however, they’ll still make criticisms, but they’ll be bogus, simply designed to generate doubt. We’ll see. A note to the press: Fix your frame To any journalists reading this: The public debate over extreme event attribution science is not going away. The science is simply too dangerous to fossil-fuel interests for them to stop fighting it. You very well might be assigned to write an article about this area of research in the future. When you do, do not automatically adopt the framing that climate misinformers want you to use. They want you to frame the story around questions like: Are climate scientists trying to put their thumb on the scale to achieve a predetermined, politically motivated result? Are climate scientists improperly letting their politics invade the science of the IPCC? That frame is a trap. Instead, you need to view this through the historical lens of the Merchants of Doubt. How does the ecosystem of doubt operate? Who funds it? What methods do they use to misrepresent science and slime researchers? What scientific results are they trying to keep people from understanding are legitimate? Ultimately, you instead need to focus your article on the generation of doubt as a way to maintain the fossil fuel industry’s social and legal license to keep burning oil, gas, and coal. If you treat the misinformers’ frame as a legitimate, good-faith scientific critique, you are helping them produce doubt. Don’t do it. Don’t be a Merchant of Doubt. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe other stuff The Staying Curious Substack has an interesting post about the different flavors of climate sensitivity. This is crucial information if you want to understand how scientists think about how much warming the Earth will experience. I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.
Pedestrians Prefer to Veer Left—and Scientists Have No Idea Why
The bias certainly appears to exist, but scientists still have no idea why it exists.
They're uninsured after Obamacare became too costly. And they're far from alone.
They’re Uninsured After Obamacare Became Too Costly. And They’re Far From Alone.
After congressional Republicans let expanded subsidies for Affordable Care Act plans expire at the end of last year, some families have decided the price is too great of a financial burden and canceled their coverage.
Rainn Wilson thinks 'The Office' couldn't be made today because it was too 'politically incorrect'
Rainn Wilson is calling out a 'bias in the media' toward 'liberal policies' for his belief that 'The Office' could not be made today, saying it would be 'too hard to be as politically incorrect as the show was.'
Mets Announce Starting Pitching Plan
After a stint in the minor leagues, the Mets are tapping Tobias Myers as their starter for Monday's game against the Reds.
The Adventures of Elliot Producer Explains Why Faie Talks So Much
'I think we may have become a little biased'
Your brain built a reality bubble. Literature, science, and philosophy can pop it.
Our brains give us a usable version of the world, not a complete one. A neuroscience and a physicist show why that gap matters for bias, free will, and the responsibility we carry into whatever happens next.
Nearly Everyone, Everywhere, Veers Left When Walking
Researchers are at a loss for why people across cultures and ages, regardless of their dominant hand, have a natural bias toward wandering in a counterclockwise direction.
Humans prefer to walk anticlockwise, scientists find
From Spain to Japan, experiments have repeatedly shown a left-turn bias, but exact mechanic ‘is still an open question’
Opinion | The Good That Can Come From Platner’s Candidacy
The Senate race in Maine is a chance to do away with inconsistent standards applied according to political bias.
Lawmakers accuse SPLC of fomenting hate, political bias
'Outnumbered' reacts as lawmakers grill Southern Poverty Law Center officials for allegedly fomenting hate, displaying political bias and targeting conservative and religious groups.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
Chelsea consider €30m deal for Juventus defender Cambiaso
Chelsea are considering a move to sign Andrea Cambiaso from Juventus after the Italian side missed out on Champions League qualification.Juventus are planning sales this summer after a sixth-place fin...
How Science Works
The 2nd Weather and Climate Livestream is now underway. It is organized by a group of early-career researchers concerned about declining funding for the field and eager to demonstrate how weather and climate research makes a difference in our lives. Last year’s event was a great success, drawing terrific press coverage including a piece in the NY Times. This year features a strong lineup of talks aimed at a general audience — I highly recommend tuning in. Share I gave a talk yesterday about How Science Works in Three Steps. You can see the short presentation here (I’ve appended a transcript below): And please tune into the livestream here. It runs through tomorrow at 6 pm ET. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe Cleaned up transcript: Thank you, and thanks to everyone tuning in. I want to talk about something I think a lot of people don’t really understand: how science actually works. Arguments over any scientific topic — climate change, vaccines, you name it — revolve around competing claims. For climate change, the claims are: Is the Earth warming? Are humans to blame? What will the impacts be? Over the last 150 years, the scientific community has developed a remarkably robust method for sorting out which claims are true and which aren’t. That method is what I want to walk through. So how does science generate knowledge? If you ask a high schooler, they’ll give you the version they learned in ninth grade: an individual scientist forms a hypothesis, runs an experiment, and draws a conclusion. That’s more or less what happens at the individual level, though in practice it’s more iterative — you design, test, and refine all at once, landing on a slightly different idea each time until you reach a conclusion about whatever hypothesis you’ve ended up with. But this isn’t science. It’s only step one. Any individual scientist can make mistakes — scientists are human. Maybe there’s a bug in the code, maybe there’s an error in the experiment. Just because someone ran an experiment and reached a conclusion doesn’t mean it’s right, and scientists don’t treat a single result as settled truth. Step two is peer review, the first layer of quality control. The scientist writes up what they did and submits it to a journal, which sends the paper to other experts in the field. Those experts judge whether the methods are sound, whether it properly references prior work, and whether the conclusions actually follow from the data. Ask any scientist and they’ll tell you peer review is miserable — the comments come back brutal, often harsh, and you have to revise and resubmit, sometimes multiple times, before an editor finally accepts the paper. Peer review catches an immense amount of bad science, but it can’t catch all of it. Reviewers get sloppy or overworked, they miss obvious mistakes, and they can’t catch hidden errors — a bug in the code, a misread instrument. So a peer-reviewed paper making it into a journal doesn’t mean the paper is right. Nobody treats a single paper as the final word. That’s where step three comes in: the crucible of science. Important results get retested by the broader community. If you publish something interesting, it gets dissected. Sometimes people try to reproduce your work directly but more often they test its implications. If someone claims X is true, and that implies that Y is also true, then scientists will go check Y. If a claim survives this gauntlet long enough, it becomes accepted. That’s how we generate knowledge. Let me give an example: the ozone hole. In 1985 — and I doubt many people watching were around then, but I remember it well — Joe Farman and his colleagues published observations from Antarctica showing that a huge chunk of ozone was disappearing every year. This was shocking because most atmospheric chemists had expected ozone loss to occur at mid-latitudes and high altitudes, not over the South Pole. When scientists see something they don’t understand, they rush to explain it, and several competing hypotheses quickly appeared in the peer-reviewed literature. One pointed to chlorine chemistry from chlorofluorocarbons. A second blamed nitrogen chemistry, possibly driven by solar proton events — meaning the sun was responsible. A third argued it wasn’t chemistry at all but dynamics: winds were carrying naturally low-ozone air from the lower atmosphere up into the stratosphere. Then the community went to work testing which theory could survive the crucible. Aircraft flew into the ozone hole and found no nitrogen, ruling out the nitrogen theory. Measurements showed the air in the polar vortex was descending, not rising — which would bring high-ozone air down, eliminating the dynamics theory. Meanwhile, those same flights measured enormous amounts of chlorine, strongly correlated with exactly the regions where ozone was being destroyed. That became known as the “smoking gun” paper, and it established that chlorine chemistry was the cause. Through this process, a consensus emerged. And it’s worth being clear that this consensus is organic. There’s no vote, no poll, no meeting where it gets decided. Scientists go to conferences, talk to one another, and independently arrived at the same answer. It’s the ultimate free market of ideas. After the consensus was established, the peer-reviewed literature doesn’t even mention the rival theories — it simply takes chlorine chemistry as given. That’s what a consensus looks like. It doesn’t mean every single scientist agrees, but it does mean there’s no longer any legitimate debate. So where can you see what scientists actually think? It’s all in the peer-reviewed literature, which contains the entire historical record of the argument: the discovery, the competing theories, the debate, the testing, and finally the community coalescing around one answer. But there’s a lot of room for misrepresentation. You can dig up an old paper claiming the ozone hole was caused by dynamics and wave it around as if there still was a debate about the cause of the ozone hole — without mentioning that the idea was disproven long ago and that even its own authors no longer believe it. There’s a bigger problem, though: the peer-reviewed literature is written in a language I call “nerd.” It’s extremely hard to read — honestly, some of it is hard for me to follow. It takes graduate students years to work through the literature in a single field, so we can’t expect policymakers or ordinary people to do it. This is where scientific assessments come in. The most famous is the IPCC — the Intergovernmental Panel on Climate Change. The job of an assessment is to read and summarize the peer-reviewed literature, synthesizing what’s settled, what’s been disproven, and what remains genuinely debated. To ensure the assessments accurately summarize the peer-reviewed literature, assessments are written by large teams, which are far less prone to bias than individuals and which dilute the influence of any one scientist with an axe to grind. The teams are deliberately assembled to avoid stacking the deck, and the reports themselves go through multiple rounds of peer review. That rigorous process is exactly why you can trust them and why people consider the IPCC to be the gold standard of what we know about the climate system. An assessment produced through a different process may well not be credible. You can even watch the consensus evolve across assessments. Take the attribution of warming. The 1990 IPCC report said the observed warming was broadly consistent with model predictions but also comparable to natural variability — in other words, we don’t know whether humans are responsible. By 1995, it concluded that the balance of evidence suggested a discernible human influence — a sentence only a committee could write, and one that barely commits to anything. By 2001, most of the warming over the previous 50 years was judged “likely” due to greenhouse gases; by 2007, “very likely”; by 2013, “extremely likely.” The 2023 report says essentially the same as 2013 — there’s not much stronger language available. So, to wrap up: science is our ultimate tool for deciphering the universe — the most successful method we’ve ever developed for determining what’s true and what isn’t. Our understanding is written into the peer-reviewed literature, so if you want to know what scientists think, that’s where to look. And for those of us who don’t speak nerd, scientific assessments are the Rosetta Stone — they translate nerd into English. Let me open the floor to questions. Thank you.
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Black Women Political Candidates Are Expected to Be 'Likable,' Qualified and Tireless. Men Aren’t.
What I had experienced during that run wasn’t entirely unfamiliar to me. The year before, in 2013, I had run for president of the Young Democrats of America (YDA), a national political party office role, against a popular opponent. The opponent was a Black man, so race wasn’t a factor in the election; however, gender was. Before my campaign, I was vice president of YDA and had heard only good things about my service: my fundraising efforts, the partnerships I had engineered with progressive organizations, and programming coordination for the membership. However, when I decided to run for president, I instantly became ‘difficult to work with’ and ‘mean.’ Research on women candidates confirms that voters are less likely to vote for a woman if they don’t like her; by comparison, voters don’t need to like men to elect them. But when I was running as the Black woman candidate in a seven-candidate primary for public office, with two other women in the race, I noticed almost nothing about my being ‘difficult’ and more about my ability and work ethic. My experience running for public office reflected the systemic bias and double standards not just for women candidates, but Black women candidates who dare to aspire to any sort of political leadership—and that needed to change. We need more progressive Black women in public office for a myriad reasons, but we also specifically need the younger generation of Instigators in office, candidates who understand the times in which we live currently. Electing more Black women will take real investment in changing the biases and attitudes (conscious and unconscious) of mostly white donors, media, campaign staff, consultants and institutional leaders to help shift the culture and systems. But this support needs to be substantively increased so that we can rebuild an inclusive, multiracial democracy with the leaders we want and need. (Excerpted from The Instigators: How Black Women Have Been Essential to American Democracy by Atima Omara.)
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Where do citizen science data come from?
Citizen science projects are playing an increasingly important role in ecological and conservation research worldwide. In these programs, members of the public support researchers by submitting photographs, audio recordings, observations, or even by contributing to data analysis. In Hungary, no fewer than 17 such initiatives combined their datasets in a joint study. The authors aimed to understand which socio-economic and environmental factors influence participation patterns in Hungary. By comparing the spatial distribution of citizen science data with an independent administrative dataset (municipality level data on income, education, demography, density and protected areas), the researchers identified several general trends as well as numerous project-specific relationships. For example, they found more per capita observations from municipalities with higher proportions of protected natural areas and lower population densities. Furthermore, excluding Budapest, the capital, both the proportion of people with diplomas and the proportion of elderly people showed a positive correlation with participation activity. In contrast, no consistent general pattern emerged regarding wealth or the proportion of children, although several project-specific relationships were observed. The study not only reveals how citizen science data are distributed across Hungary, but also provides robust support for the view that various biases must be taken into account when analyzing such data. The authors hope that their findings will help researchers in other countries design more effective projects and produce more accurate analyses by accounting for some of these or similar biases.
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New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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OPEN THREAD
The Daily Merengue is a place where you can feel free to discuss all things football. Do not be alarmed by the overt RMCF bias. It’s in the name! Shoutout to the mods who do a fantastic job Kung_Fu_Zizou, Juninho, NeRObutBlanco, Felipejack, Ezek Ix and yours truly, Valyrian Steel. El-Clasico – Real Madrid return to […]
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Pistons vs Magic preview: It’s time for Game 7
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‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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Navigating the social media minefield
Scientists and researchers seeking to announce promising findings, exchange ideas or contribute to policy discussions outside of academic circles may find social media an expedient tool.But increasingly, scientists and other professionals are discovering that online platforms can be a double-edged sword: while sharing their work and expertise is crucial for visibility and meeting funder expectations, it also exposes them to baseless criticism and even accusations of spreading false information."It is getting harder and harder for scientists, and researchers in general, to convey their findings without facing attacks on their legitimacy and accuracy of their research," says Valère Ndior, a professor of public law at the University of Western Brittany in Brest, France, who has spent more than a decade studying misinformation on social media. Researchers attempting to challenge deceptive online information may themselves be accused of political bias or not being objective in their research, he explains, adding that some scientists disengage because they feel overwhelmed and unable to sustain meaningful dialogue. "It is quite sad to see in some instances that researchers end up leaving social media altogether because they feel like the pressure is getting too intense or feel threatened," he says.Ndior, a past president of the Francophone Network of International Law, is also involved in European efforts to make science and research findings more accessible to the public. As an ambassador for the Science Comes to Town (SCTT) project, he has participated in recent discussions on public perceptions of science and online misinformation. The European Commission-funded initiative connects Brest and two other cities — Kiel in Germany and Split in Croatia — and aims to strengthen ties between communities, universities and research institutions to promote science awareness and cooperation across Europe.Coordinated campaigns around contentious topicsNdior draws a clear distinction between critical thinking and thoughtless criticism. Yet he sees online discussions being increasingly shaped by suspicious behaviour and coordinated information campaigns — including "raids" aimed at dominating discussions and "astroturfing," where small groups act as larger movements to sway debates. Tracking these patterns has become harder because some platforms have restricted data or charge for it, despite provisions of the European Union's Digital Securities Act aimed at ensuring researchers can have access to such data. According to Ndior, "platforms became more reluctant from the moment access to data became a legal requirement rather than a voluntary initiative."Unfounded claims and scepticism about science existed long before Internet chat groups. But the noticeable rise in online hostility has intensified around contentious topics, such as the coronavirus pandemic and climate change, alongside the weakening of gatekeepers to moderate content.For Ndior, dubious claims about pandemic countermeasures and vaccine safety highlighted the struggle between "balancing freedom of expression and the protection of public health and order." Efforts to remove social media content deemed false or risky for public health triggered accusations of censorship and the silencing of legitimate discussion, and in some cases, legal challenges.Concerns about attacks on the credibility of scientists and researchers have generated a flurry of academic papers in recent years and prompted professional communities to address ways to counter online attacks. Such unease extends to the generation that has grown up with digital media. Ndior points out that his students, some of whom research artificial intelligence regulation, hate speech, online surveillance and other controversial topics, hesitate to share their work or viewpoints online. "Many of them don't want to expose themselves too much because they don't feel ready to be faced with the backlash of other people criticising their research," he explains.Safer harbours for engagementLegal challenges against false or defamatory posts are not necessarily a solution — especially given the trans-jurisdictional nature of the Internet. Lawsuits also take time and money, and if they fail, can be counterproductive. Instead, Ndior advises scientists and researchers to be selective in how and where they communicate. Professional online platforms and moderated discussion spaces offer a better environment for engagement. He also highlights the value of more traditional formats, such as radio interviews and in-person discussions at schools, libraries and science fairs, which encourage dialogue with the public. "Personal contact makes a big difference," he says, while acknowledging that these approaches lack the global reach of social media.Ndior’s interest in digital law and online misinformation stems from his time as a doctoral student in international law in the early days of social media. He began analysing posts from governments and heads of state to determine if they could be considered official and thus used as evidence in litigation. His work attracted media attention and led to conference and speaking invitations. "This was not part of my plan," he says, "but this is what happens as a researcher — your interest is sparked by a certain event or phenomenon, and it suddenly becomes your focus."Ndior believes SCTT provides "a double benefit" for researchers, enabling them to connect with peers and interact with the public. "There is a wide community of people who are not in academia who are critical thinkers looking for opportunities to ask questions, to get information and contribute their ideas,” he says. "I really value that kind of feedback."Science|Business, which connects policymakers, academics and industry through its news reporting, analysis and events, is an SCTT partner.
Navigating the social media minefield
Scientists and researchers seeking to announce promising findings, exchange ideas or contribute to policy discussions outside of academic circles may find social media an expedient tool.But increasingly, scientists and other professionals are discovering that online platforms can be a double-edged sword: while sharing their work and expertise is crucial for visibility and meeting funder expectations, it also exposes them to baseless criticism and even accusations of spreading false information."It is getting harder and harder for scientists, and researchers in general, to convey their findings without facing attacks on their legitimacy and accuracy of their research," says Valère Ndior, a professor of public law at the University of Western Brittany in Brest, France, who has spent more than a decade studying misinformation on social media. Researchers attempting to challenge deceptive online information may themselves be accused of political bias or not being objective in their research, he explains, adding that some scientists disengage because they feel overwhelmed and unable to sustain meaningful dialogue. "It is quite sad to see in some instances that researchers end up leaving social media altogether because they feel like the pressure is getting too intense or feel threatened," he says.Ndior, a past president of the Francophone Network of International Law, is also involved in European efforts to make science and research findings more accessible to the public. As an ambassador for the Science Comes to Town (SCTT) project, he has participated in recent discussions on public perceptions of science and online misinformation. The European Commission-funded initiative connects Brest and two other cities — Kiel in Germany and Split in Croatia — and aims to strengthen ties between communities, universities and research institutions to promote science awareness and cooperation across Europe.Coordinated campaigns around contentious topicsNdior draws a clear distinction between critical thinking and thoughtless criticism. Yet he sees online discussions being increasingly shaped by suspicious behaviour and coordinated information campaigns — including "raids" aimed at dominating discussions and "astroturfing," where small groups act as larger movements to sway debates. Tracking these patterns has become harder because some platforms have restricted data or charge for it, despite provisions of the European Union's Digital Securities Act aimed at ensuring researchers can have access to such data. According to Ndior, "platforms became more reluctant from the moment access to data became a legal requirement rather than a voluntary initiative."Unfounded claims and scepticism about science existed long before Internet chat groups. But the noticeable rise in online hostility has intensified around contentious topics, such as the coronavirus pandemic and climate change, alongside the weakening of gatekeepers to moderate content.For Ndior, dubious claims about pandemic countermeasures and vaccine safety highlighted the struggle between "balancing freedom of expression and the protection of public health and order." Efforts to remove social media content deemed false or risky for public health triggered accusations of censorship and the silencing of legitimate discussion, and in some cases, legal challenges.Concerns about attacks on the credibility of scientists and researchers have generated a flurry of academic papers in recent years and prompted professional communities to address ways to counter online attacks. Such unease extends to the generation that has grown up with digital media. Ndior points out that his students, some of whom research artificial intelligence regulation, hate speech, online surveillance and other controversial topics, hesitate to share their work or viewpoints online. "Many of them don't want to expose themselves too much because they don't feel ready to be faced with the backlash of other people criticising their research," he explains.Safer harbours for engagementLegal challenges against false or defamatory posts are not necessarily a solution — especially given the trans-jurisdictional nature of the Internet. Lawsuits also take time and money, and if they fail, can be counterproductive. Instead, Ndior advises scientists and researchers to be selective in how and where they communicate. Professional online platforms and moderated discussion spaces offer a better environment for engagement. He also highlights the value of more traditional formats, such as radio interviews and in-person discussions at schools, libraries and science fairs, which encourage dialogue with the public. "Personal contact makes a big difference," he says, while acknowledging that these approaches lack the global reach of social media.Ndior’s interest in digital law and online misinformation stems from his time as a doctoral student in international law in the early days of social media. He began analysing posts from governments and heads of state to determine if they could be considered official and thus used as evidence in litigation. His work attracted media attention and led to conference and speaking invitations. "This was not part of my plan," he says, "but this is what happens as a researcher — your interest is sparked by a certain event or phenomenon, and it suddenly becomes your focus."Ndior believes SCTT provides "a double benefit" for researchers, enabling them to connect with peers and interact with the public. "There is a wide community of people who are not in academia who are critical thinkers looking for opportunities to ask questions, to get information and contribute their ideas,” he says. "I really value that kind of feedback."Science|Business, which connects policymakers, academics and industry through its news reporting, analysis and events, is an SCTT partner.
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OPEN THREAD
The Daily Merengue is a place where you can feel free to discuss all things football. Do not be alarmed by the overt RMCF bias. It’s in the name! Shoutout to the mods who do a fantastic job Kung_Fu_Zizou, Juninho, NeRObutBlanco, Felipejack, Ezek Ix and yours truly, Valyrian Steel. Gameday!! Huge clash in the context […]
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Forum on harnessing Artificial Intelligence for health equity
Register to this forumOver the past decade, artificial intelligence (AI) has significantly evolved in complexity, performance, and utility. Its integration across a wide range of industrial sectors has accelerated, with healthcare emerging as one of the most transformative sectors. In this sector, AI has enabled high-impact applications – from enhancing the speed and accuracy of disease diagnosis and supporting clinical decision-making, to strengthening public health initiatives such as disease surveillance and workforce optimization. Moreover, AI holds immense potential to empower individuals, enabling them to take greater control of their personal health and well-being. Yet the central question remains: Who benefits, who is at risk, and do these innovations close or widen health inequities? While the potential of AI-enabled applications is substantial, many countries have approached their adoption with caution. This hesitancy stems from concerns about risks and benefits, including model bias, data breaches, privacy violations, and the erosion of professional skills due to over-reliance on AI. Additional barriers include inadequate data infrastructure and a lack of robust evidence on the most effective forms and interfaces of AI to enhance service delivery and system performance. Moreover, the rapid rise of Generative AI has introduced a heightened urgency for the development of comprehensive governance and ethical frameworks. The Asian Development Bank (ADB) Health Practice Team and Digital Sector, in collaboration with the World Health Organization (WHO) Regional Office for the Western Pacific, are convening the Forum on Harnessing AI for Health Equity on 25–26 March at ADB Headquarters. Senior health officials from 16 developing member countries (DMCs), alongside leading researchers, innovators, and policymakers from across Asia and the Pacific, will engage in discussions on how AI can be governed, financed, and scaled to deliver healthier lives for all. The forum is funded through the High-Level Technology Fund with support from the Government of Japan. Objectives The two-day forum aims to:Identify high-impact, evidence-based health AI use cases suitable for resource-constrained settings and map out a pipeline for potential supportIntroduce and validate the WHO's policy guidance on minimum necessary ecosystem for countries and institutions to self-assess readiness for AI adoptionObtain initial commitment from participating stakeholders to join a pilot regional initiative focused on data/model sharing for a priority health challengeEndorse a prioritized set of actionable policy recommendations focusing on governance, regulation, and sustainable financing necessary to scale high-impact health AI use cases in resource-constrained settings Target participants The forum will convene participants from 16 ADB DMCs including Bangladesh, Bhutan, Cambodia, Cook Islands, Fiji, Indonesia, Lao People's Democratic Republic, Mongolia, Nepal, Palau, Papua New Guinea, Philippines, Sri Lanka, Thailand, Uzbekistan, and Viet Nam. In addition, health system leaders and practitioners, private sector technologists, academic experts, representatives from civil society organizations, development partners, and philanthropic institutions engaged in AI for Health from Australia, the People’s Republic of China, Japan, Malaysia, New Zealand, Singapore, and the Republic of Korea will also be invited. SessionsDay 1 (25 March)Session 1: AI Use Cases for the Care Continuum: Improving Access and QualitySession 2: Adopting a risk-based approach for AI use case prioritizationSession 3: Establishing the Minimum Ecosystem for Implementation Risk ManagementSession 4: Future-Ready Health Workforce: Building Competency for Human-AI CollaborationDay 2 (26 March)Session 5: The Political Economy of AI in Health: Understanding Stakeholder Interests and their Impact on EquitySession 6: AI and Health Financing: Supporting Fair Coverage and ProtectionSession 7: AI Regulation and Oversight: Pathways for Different RealitiesSession 8: The Way Forward – Building Alliances and Regional Partnership How to registerThe forum will be held in a hybrid format. In-person attendance is by invitation only, while livestreaming of the forum will be available to the public. To register, please complete this form on or before 24 March 2026.Event organizers / partnersAsian Development Bank (ADB)WHO Regional Office for the Western Pacific High-Level Technology Fund (HLTF)ContactAkihito WatabeHealth Specialist, SD3-HSDawatabe@adb.org Jae Kyoun KimHealth Specialist, SD3-HSDjkkim@adb.org
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