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How Sen. Richard Neuberger transformed Oregon politics
Historian Stephen Forrester examines how Richard Neuberger reshaped Oregon politics and advanced conservation in the U.S. Senate.
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Dr. John Carter, an Ear, Nose, and Throat physician and chair of Otolaryngology at Ochsner Medical Center, holds a tiny scope used to examine breathing passages on infants on Monday,
Transitions in Health Insurance Status Following Initiation of Dialysis by Modality
Researchers examined whether dialysis modality affected patients' likelihood to experience an insurance transition.
Iran war strains global business travel, as companies adjust meeting plans
Global conflicts are causing some companies to examine their “duty of care” policies and could bring back more virtual meetings, the survey found.
Long-deferred projects move forward as Chicagoland communities build for the future
Communities throughout Chicago are modernizing public facilities as part of a broader construction effort to build for function and longevity.
Democrats slammed for 'dehumanizing' rhetoric amid concerns about political-fueled violence
Political analyst Gianno Caldwell discusses dangerous political rhetoric and its consequences, including an assassination attempt on President Donald Trump. He also examines how extreme language and socialist policies impact the country.
Healthcare cost surge makes generous parental paid leave benefits a target of workplace cuts
As healthcare costs soar and companies search for savings, generous paid parental leave is a benefit being cut, with recent examples from Zoom and Deloitte.
‘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
ASK A COMMISSIONER: How Do I Support Local Businesses and Stimulate the Economy?
By Mary Faith Bell, Tillamook County Commissioner Recently a constituent asked me on Facebook to provide examples of things I have done in my current term to stimulate the economy and support…
2026 Cram for the Exam AP Government & Politics Exam Review
Former C-SPAN Teacher Fellow and Troy, Michigan teacher Ryan Werenka and Petaluma, California teacher Kevin Jackson answered students' questions about the upcoming Advanced Placement U.S. Government and Politics Exam and shared tips on how students can best prepare for the test.
Why do crabs walk sideways? Scientists trace it back 200 million years
Crabs’ famous sideways walk may trace back to a single evolutionary moment 200 million years ago. Researchers found that most modern crabs inherited this trait from one ancestor—and never looked back. The movement likely gave them an edge, helping them dodge predators with quick, unpredictable bursts. It’s a rare example of a behavior evolving once and then dominating an entire group.
Apple Stops Offering Mac Mini With 256GB of Storage, Starting Price Rises to $799
Apple this week stopped offering a 256GB storage option for the Mac mini worldwide. As a result, the desktop computer now has a higher starting price. In the U.S., for example, the Mac mini now starts at $799 with the M4 chip, 16GB of RAM, and 512GB of storage, whereas it previously started at $599 with the M4 chip, 16GB of RAM, and 256GB of storage. Mac mini models with the M4 Pro chip already had a minimum of 512GB of storage, so there is no change to pricing for those configurations.
What’s Safe to Eat? Birds of a Feather Learn Together
In a study, Australian cockatoos figured out that a new food was OK to consume by observing one another, a vivid example of “social learning” in animals.
4 things you missed from Musk v. Altman trial this week
Elon Musk faced another day of cross-examination. Jared Birchall, Musk's wealth manager, was the next witness called in the blockbuster OpenAI trial.
Top Doctor Draws Attention to Trump’s ‘Overdue’ Health Exam
The president had two “routine” check-ups last year.
Venezuela and Colombia: Immigration Options for Global Employers Renewing Business Activity in the Region (Video Chat)
Our attorneys examine how Venezuela and key neighboring jurisdictions—particularly Colombia—are reshaping immigration, employment, and workforce
Five first-rounders headline most questionable 2026 NFL Draft picks for every team
Examining the one pick for every team that raised eyebrows during the 2026 NFL Draft.
Regions Bank Names Veteran Banker Amy Barrentine as Head of Regions Business Capital
Barrentine brings over 30 years of industry experience to lead teams delivering asset-based lending solutions to clients. Regions Bank on Thursday announced Amy Barrentine has been elevated to serve as head of Regions Business Capital , a specialty finance team within the bank’s Corporate Banking Group . Regions Bank announced veteran banker Amy Barrentine has been elevated to serve as head of Regions Business Capital, a specialty finance team within the bank’s Corporate Banking Group. An important competitive differentiator for Regions Bank, Regions Business Capital develops tailored services for companies at all stages of their life cycles. From managing periods of rapid growth, to navigating performance volatility, strategically leveraging acquisitions, to managing shareholder distributions, and more, Barrentine’s team specializes in helping clients build responsible growth and achieve clear goals. Examples of solutions provided by the team include: Asset-Based Lending :
SF to get a pair of new outdoor booze zones
Entertainment zones popping up in the upper Fillmore and Glen Park.
Viewpoint: A career in science is possible, and worth it
Starting a career in science is exciting, but also demanding, with researchers often faced with intense competition, long working hours and high levels of stress. A pioneer in the field of mass spectrometry, Carol Robinson has been there. She speaks with Science|Business on the importance of creating a supportive environment, particularly for women.Robinson did not wake up one day thinking that she would become a scientist. It was only late in school that she developed an interest in chemistry. “The more you work at something, the more you think, actually, this is fascinating. So, I went much further and deeper than I ever needed to pass the exam. And then I loved it,” she says. “I thought, I’m going to give it my all. I’m going to enjoy it while I’m here. But I didn’t think this would be my career for 50 odd years.”She left school at 16 to become a lab technician with the drug company Pfizer, which is where she first worked with mass spectrometry. Fascinated, she was encouraged to take a part-time degree and then a PhD. She went on to do pioneering work in native mass spectrometry, a technique that is used to study protein structures in their natural state, paving the way for breakthroughs in drug discovery. She also broke new ground by becoming the first female professor of chemistry at the University of Cambridge, and then the first female professor of chemistry at the University of Oxford, her current home.She now wants to show young researchers that a career in science is worth it. “Sometimes, I believe the culture isn’t very encouraging, and that’s something I have worked on,” Robinson says.In 2021, she launched the Kavli Institute for Nanoscience Discovery at Oxford, whose acronym gives a sense of what she hopes it will achieve. “Maybe it is corny,” she says. “It’s just a reminder that I would like it if we could all be kind to each other and supportive, especially for early-career researchers of either gender, because there’s quite a big attrition rate in science, and maybe it just doesn’t feel like a very friendly place to work.”And yet, a career in science comes with a lot of flexibility. “There are few careers where you have that amount of freedom, to go to conferences, to the library. Just because you didn’t see me didn’t mean I wasn’t working,” she says. “And I’m excited to come in every day. So, I don’t want people to think it’s a terrible place.”Gender equality is also important when it comes to the quality of science as a career. “The more women we have, the better it becomes,” Robinson adds.Imposter syndromeAccording to EU data, women outnumber men in university education but account for just about a third of graduates in science, technology, engineering and mathematics (Stem). And while the number of women working as scientists and engineers in the 27-member bloc is on the rise, their representation remains below that of men, at 41%. This discrepancy is even more pronounced in top-tier functions across these fields, where women fill just 20% of positions.Having supervised many young scientists, Robinson sees that women continue to have a greater propensity than men to doubt their abilities, a feeling she once shared. “When I was doing my PhD at the University at Cambridge, I really felt like an imposter,” she says. “I don’t think I ever felt I deserved to be there or owned it and was proud. I just thought I was lucky.” Later on, she adds, “I was always that person who said, I can’t apply yet because my CV is not strong enough.”She has now made it her job to encourage women to break with this imposter syndrome. For example, rather than calling for volunteers to give a lecture in the groups that she mentors or teaches, which nearly always results in men stepping forward, she goes straight to the women, “because I know they won’t push themselves forward.”But the gender gap in science is not just down to imposter syndrome. The “obvious” reason, Robinson says, and the reason that is on everybody’s mind when discussing workplace gender equality, is the balance between work and raising a family. Her own path was to choose between the two, taking an eight-year break after completing a postdoctoral training fellowship to bring up three children. And she continues to advise women not to work part-time while they raise a family. “You’ll just get half the pay and continue to work full-time because you are not able to fit everything in,” she says. “You should be able to have a family and come back to have the career that you want.”The solution is for men to also take career breaks. “That kind of evens it up, so that it’s not such a big thing for women,” Robinson says. “I always encourage men in my groups to do that. I think it’s important to take your share of the duties.”Support produces resultsAccording to the Women in Stem Network, a UK advocacy group, countries with strong childcare systems and equal pay policies retain more women in technical careers. In Europe, these are primarily found in the Nordic region and parts of central Europe.At the EU level, the European Commission asks all Horizon Europe applicant organisations to have a gender action plan in place. Since this criterion was introduced in 2021, the share of women leading consortia has increased to 31%, against 23% in Horizon 2020. Their overall participation, however, has risen just 1%, to 38%. After trying to weaken the requirement for gender action plans in 2025, the Commission now intends to adopt a plan for supporting women in research and innovation.Related articlesCommission scraps plan to water down gender requirements in HorizonHow gender balance can swing who wins Horizon Europe fundingRobinson herself has benefited from three Advanced Grants from the European Research Council. “I was extremely honoured and privileged to get those, because they really allowed me to fuel my passion,” she says. “Without that, it would have been a much harder slog.”Beyond applying gender rules, she says that more work is needed on translational enterprise, where women are, once again, largely underrepresented. The European Patent Office found that only 13.8% of applications were filed by women in 2022. “We have so many more women doing amazing science; why do they feel it’s not a thing to have a company?” she asks.She founded her own spin-out company, OMass Technologies, in 2016, and remains chair of its scientific advisory board now that it trades as OMass Therapeutics. As for research, Robinson is now studying how changes in the brain may be linked to depression, using port-mortem tissue. “We’re hoping to publish soon,” she says. “We had to consult with a lot of people who knew a lot more about the brain than we did. So, we’ve learnt a lot, and that’s always a lot of fun to learn a new thing. That’s what keeps you going.”
Tulane Professors Examine “Seismic Shift” in College Sports in Harvard Law Journal
Tulane University Law School News
FDA Releases Results from Largest-Ever Testing of Infant Formula in the U.S.
FDA announced results from the largest and most rigorous examination ever conducted focused on chemical contaminants in infant formula available on the U.S. market.
Merman and the New Rules of Branded Entertainment
Little Black Book, With roots in premium TV and a foothold in advertising, Merman is redefining branded entertainment. LBB’s Olivia Atkins examines how the production company blends storytelling, talent and craft to create work that earns attention, travels across platforms and delivers long-term brand value
King Charles’ subtle but striking warning to America
In a new age of revolt, it took a king to remind America of its republican values: the rule of law, democracy and the power of its international example.
San Francisco State University Partners with UC Law San Francisco to Expand Downtown Presence - UC Law San Francisco (Formerly UC Hastings)
SFSU’s Lam Family College of Business will use classroom and office space at 198 McAllister Street to establish a new downtown campus. San Francisco State University (SFSU) and UC Law San Francisco have reached an agreement that establishes a new downtown home for SFSU’s Lam Family College of Business at UC Law SF’s Academic Village. The agreement places SFSU business students in close proximity to leading employers, industry partners and transit hubs, enhancing opportunities for internships, networking and career advancement. The UC Law SF Academic Village is a shared-campus model designed to foster collaboration among higher education institutions and contribute to San Francisco’s ongoing revitalization. San Francisco State University (SFSU) and UC Law San Francisco have reached an agreement that establishes a new downtown home for graduate business programs from SFSU’s Lam Family College of Business at UC Law SF’s Academic Village. The agreement creates a new downtown campus location for SFSU, giving students direct access to San Francisco’s thriving business environment. Beginning in Fall 2026, graduate business students will take classes at the UC Law SF building at 198 McAllister Street, at the nexus of the Civic Center, Mid-Market and Tenderloin communities. Under the agreement, SFSU will occupy 3,200 square feet of exclusive teaching and office space and will share additional classrooms with UC Law. The move positions students in close proximity to leading employers, industry partners and transit hubs, enhancing opportunities for internships, networking and career advancement. “This partnership is an example of the growing strategic collaborations that expand opportunities for our students,” said SFSU President Lynn Mahoney. “By situating graduate business programs in the city’s urban core, we will help students build connections, gain experience and achieve their goals for meaningful careers and upward mobility.” Students enrolled in select Lam Family College of Business graduate programs will have access to the Academic Village’s modern classrooms, conference facilities, library, study spaces and resident and event support services. The site also includes lounges, on-site dining and newly built, fully furnished, apartment-style housing for students and professionals. As institutional partners, SFSU College of Business students and alumni are eligible for tuition discounts if they enroll in the UC Law SF Master of Legal Studies program, designed for professionals whose work intersects with the law. “San Francisco State University is an excellent addition to our Academic Village, and we are excited to have the Lam Family College of Business join us in the fall,” said UC Law SF Chancellor & Dean David Faigman. “The partnership advances UC Law SF’s commitment to collaborate with California’s other great public schools and adds vibrancy and energy to the heart of the city. Our students and programs will benefit from the interdisciplinary opportunities that will arise.” The UC Law SF Academic Village is a shared-campus model designed to foster collaboration among higher education institutions and contribute to San Francisco’s ongoing revitalization. The Lam Family College of Business will begin occupying its new space there in July 2026, with classes beginning in August. The move continues SFSU’s longstanding presence in downtown San Francisco and reflects a broader effort to align academic programs with the needs of both career-minded students and the region’s expanding and evolving workforce. For more than 60 years, the Lam Family College of Business has delivered high-quality, AACSB-accredited business education across the San Francisco–Silicon Valley region. Its graduate programs — including the MBAand the STEM-designated Master of Science in Business Analytics and Master of Science in Accounting and Analytics — are designed to help students develop the skills they need to navigate the complex challenges of a global, data-driven economy. About San Francisco State University San Francisco State University is a public university serving students from the San Francisco Bay Area, across California and around the world, with nationally acclaimed programs that span a broad range of disciplines. More than 20,000 students enroll at the University each year, and its more than 304,000 graduates have contributed to the economic, cultural and civic fabric of San Francisco and beyond. Through them — and more than 1,300 world-class faculty members — SFSU proudly embraces its legacy of academic excellence, community engagement and commitment to social justice. For more information, visit sfsu.edu. About UC Law SF UC Law San Francisco is a public law school of the University of California, founded in 1878 as the first law school in the UC system. Located in San Francisco’s Civic Center, it offers a comprehensive legal education with a strong emphasis on experiential learning, public service and professional preparation. Through its Academic Village, UC Law SF brings together students, faculty and partner institutions in a collaborative campus environment in the heart of the city. Other Academic Village partners include UC San Francisco, UC Davis, the University of San Francisco and the University of the Pacific Dugoni School of Dentistry. For more information, visit www.uclawsf.edu.
Xbox boss Asha Sharma hints memory costs "will impact" pricing and availability of next-gen Project Helix console
Xbox boss Asha Sharma is carefully examining the memory crisis situation as the brand makes plans for Project Helix's launch in the future.
Food As Medicine Conference Highlights Power of Nutrition to Transform Community Health
Hofstra University brought together health leaders, researchers, and community partners during National Public Health Week (NPHW) to examine a simple but powerful idea that people can shape how they live, heal, and thrive through what they eat. The Food As Medicine Conference, held earlier this m
After correspondents’ dinner shooting, expert looks at patterns behind political violence
The shooting during the White House correspondents’ dinner Saturday is another example of how acts of political violence are becoming a recurring part of the national landscape.
Neanderthals' brains weren't to blame for their demise, new study suggests
Researchers examining the brains of living people found that they differed more substantially than Neanderthals' brains differed from modern humans', calling into question the reason our evolutionary cousins mysteriously disappeared.
Rachel Reeves considering rent freeze to limit Iran war fallout
Exclusive: Sources say chancellor is examining exceptional measures to protect household budgets
Report says legalizing online sports betting in Nebraska could generate $87M over 5 years
Research commissioned by a group petitioning to legalize online sports betting in Nebraska found it could generate $87 million in new taxes over five years.
I’m a fourth-year med student, but I only learned one historical example of medical racism
Modern randomized controlled trials often rely on marginalized populations abroad.
The Lancet Global Health: Untangling the complex relationship between HIV-exposure and TB in children: A narrative review
A review examined existing literature on TB prevention, prevalence, and outcomes in CHEU, identifying key knowledge gaps to guide future research.
Post-Pelosi, status quo out, candidate says
Former boss Alexandra Ocasio-Cortez hasn't endorsed.
Friend, not foe: The health threat from spiders is low in W. Pa., experts say
This is the latest installment of Pittsburgh Pests, an occasional series examining the insects and other creatures that pose health risks in our region. Spiders...
Overnight Science Digest: Rethinking thinking by examining animal and AI intelligences
This week’s science news theme—the convoluted relationships between humans and non-human intelligences—arose from an unexpected combination of two dichotomous groups, wildlife and AI. I’ve added in a…
New synthetic opioid linked to overdose death in San Francisco
The drug is reportedly 10 times more potent than fentanyl.
How Investors Are Reacting To Sphere Entertainment (SPHR) Upgraded Earnings Momentum And Analyst Sentiment
Sphere Entertainment recently attracted attention after Zacks assigned it a Momentum Style Score of A and a Zacks Rank of #2 (Buy), reflecting improved analyst sentiment and positive earnings estimate revisions in the period leading up to today. This shift in sentiment highlights how changing expectations for Sphere’s earnings power and momentum profile can influence how investors view its longer-term prospects. Next, we’ll examine how this stronger earnings momentum, highlighted by upgraded...
How I booked a $15,000 around-the-world trip in business and first class using points
Learn how to book a round-the-world trip in business and first class using points and miles, including real flight examples, transfer strategies and expert tips.
Status symbols men are obsessed with in 2026
Status symbols for men in 2026 are all about looking sharp and evoking quiet luxury. Leather bags and tailored designer suits are just two examples.
How Gary Neville and Gary Lineker became rival podcast moguls
BBC sports editor Dan Roan examines how Gary Lineker and Gary Neville became two of the biggest players in a rapidly changing media industry.
How Elon Musk Used SpaceX to Benefit Himself and His Businesses
The rocket maker has been a useful financial tool for Mr. Musk, providing the billionaire with loans and aiding his struggling companies, a Times examination found.
How Elon Musk Used SpaceX to Benefit Himself and His Businesses
The rocket maker has been a useful financial tool for Mr. Musk, providing the billionaire with loans and aiding his struggling companies, a Times examination found.
Wisconsin DOJ sues online prediction markets, charging illegal sports betting
The lawsuits were filed as Wisconsin prepares to renegotiate tribal gaming compacts to include online sports betting under a new state law.
The intersection of music in sports: behind the curtain
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Are Health Insurance Companies the Reason for Our Health System’s Ills?
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Shaky political science: using "simulated" elections over real-world elections
[Dear DSOS readers – this article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in early December, we examined over 40 studies focused on ranked choice voting. We found that many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data, and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first three articles in our series: the first one summarizing the overall results of our report, the second one analyzing a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
From Song to Science
Briana Nave is challenging what it means to be musically talented by examining how talent has been defined historically.
The Death of Public Health
During the recent measles outbreak, the largest in more than 30 years, Robert F. Kennedy Jr. refused to clearly recommend the measles vaccine, saying instead that it was a “personal decision,” and that parents should “do their own research.” Similarly, during her recent confirmation hearing for United States Surgeon General, Casey Means was questioned by Senator William Cassidy (R, La). “You’re the nation’s doctor,” said Cassidy. “Would you encourage [a mother] to have her child vaccinated!” “I’m not an individual’s doctor,” Means said. “And every individual needs to talk to their doctor before putting a medication in their body.” In June 2025, Marty Makary, Commissioner of the Food and Drug Administration (FDA), said that COVID vaccines for pregnant women should be a “personal choice”, even though every other country in the world recommended the COVID vaccine during pregnancy due to a higher risk for severe disease. It doesn’t end there. On January 5, 2026, RFK Jr. reduced the number of routinely recommended vaccines from 18 to 10. For vaccines that protected against influenza, COVID, and rotavirus, parents were urged to speak with their doctors—to make an individual choice. (On March 16, 2026, in a ruling by a federal court in Boston, RFK Jr.’s revised schedule was invalidated.) What’s happening? Why are public health officials moving away from making recommendations for the public? The origin of this change might be rooted in the first two years of the COVID pandemic. In 2020, during the first year on the pandemic, we didn’t have anything to stop a virus that was killing hundreds of people a day and could be spread asymptomatically. We didn’t have antivirals until October, monoclonal antibodies until November, and vaccines until December. All we could do was restrict travel, close businesses, shutter schools, and mask, isolate, quarantine, test, and social distance. During the second year of the pandemic, when we had vaccines to prevent COVID, people couldn’t go anywhere without their vaccine cards. They couldn’t go to their favorite bar or restaurant or sporting event or place of worship without proof of vaccination. Some people were fired from their jobs for refusing to get vaccinated. For many, this was seen as massive government overreach and energized what has been called the “medical freedom movement.” Indeed, a headline in the New York Times declared, “How Health Freedom Became a Winning Rally Cry.” As expressed in the comments of RFK Jr., Casey Means, and Marty Makary, the medical freedom movement focuses on the individual not the community—the opposite of public health. In an article titled “Ethics and Social Value Judgments in Public Health,” ethicists from Yale University distinguished public health from medical freedom. “Public health policies,” they wrote, “are population oriented. Because individual health affects the health of others, public health measures regulate individual behavior to achieve population health goals. All raise questions about how individual autonomy and freedom should be balanced against public health interests…which respects individuals’ sovereignty over their bodies and actions as long as their actions do not harm others.” The medical freedom movement fails to recognize that it often limits the freedom of others. For example, a parent who is afraid to send a child to school where many of the students are unvaccinated. Or immune compromised children who depend on those around them to for protection. The question then becomes “Freedom for whom?” Is it your right, in the name of medical freedom, to catch and transmit a potentially fatal infection—and in so doing, take away the freedom of others? In Four Essays on Liberty, Isaiah Berlin sums it up best. “Liberty for the wolves,” he wrote, “is death to the lambs.” Thanks for reading Beyond the Noise! Subscribe for free to receive new posts and support my work. Subscribe
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Seniors: Dennis Ouellette, 80
Ontario Orchards owner not slowing down as he grows his farm market By Stefan Yablonski The seeds for Dennis Ouellette’s career path were sown when he was 6 years old — and he isn’t slowing down. “Since age 6, I grew up an agriculturalist. That is 72 years. Started out with a 37-acre farm when I bought it from my parents and built it up to almost 300 acres. Brought it back down to around 200 acres now,” he said. The location of his business was just a barn, a horse barn, he said. “In 1967, it officially became Ontario Orchards Farm Market. My wife [June] and I graduated [high school] in 1964 and three years later we became Ontario Orchards. We have been here more than 60 years,” he explained. “It is the same location. We have expanded over the years as the customers mentioned different things; we have expanded to bring in different products. “What keeps me going is I totally enjoy what I am doing. I enjoy the people that I work with. The customers are wonderful. Mostly, more important than anything, is my family — my wife, my daughters, grandchildren, son-in-law — we are still very much a family business I’m not going to retire just yet.” He also enjoys the changing seasons. “Ontario Orchards is a four-season business. There is always something new; more energy, more interest. Not only for the business but for my staff, the people that work here. Things keep changing, changing. We are always looking forward to something new and exciting,” he said. They work with quite a number of local farmers within the area and throughout the state, throughout the country and from around the world.” “We do have things from different countries around the world. But specifically, we are very excited about the local. We have an over 200-acre farm. We sell just about everything we raise on that farm right here at the farm market. For example, we press cider year-round. We make baked goods every day, year-round. We sell our apples 12 months a year.” “Today, there are so many products in Ontario Orchards that we never would have thought of in 1967,” he continued. “I attribute a lot of that to my daughters. I like the young blood and new energy. That is very crucial to a business, it keeps it vibrant.” What’s his favorite part of the job? “I don’t have a favorite part. I enjoy it all. I have a lot of intertest in other things,” he said. “I have staff that knows so much. I can go to the staff and say, ‘I don’t know what this is, you tell me what this is.’ They are so knowledgeable. It is impossible for one person to know everything in this place. In the summer, staff is about 60; in the winter about 20, 25.” He added that he plans on being around for several more years.
Seniors: Carol Watson, 72
Owner of Carol Watson Greenhouse and Landscaping in LaFayette enjoys her growing business — still puts in six-, seven-day weeks By Mary Beth Roach Carol Watson, the owner of Carol Watson Greenhouse and Landscaping, has been in gardens since she could walk. Literally. She said she learned to walk in her grandfather’s garden, which was next door to her current home and business in LaFayette. Today, at 72, she is running the business that her mother, Claire, opened in 1981 as Watson Greenhouse. Watson worked part-time for her mom until 1990, when she joined her full time. While she may not have had any formal horticultural experience, she said she learned so much about growing and raising plants from her mom, who had an abundance of energy and talent. Now, after 35 years in the business full time, Watson has no plans to retire, although she joked that she’d like a couple of days off a week. On her seven-acre site, complete with four greenhouses, Watson said her favorite parts of the job are working with and educating her customers; helping some of her young customers build terrariums; conducting occasional class field trips; and supporting local art by decorating the shop with an eclectic mix of pottery from at least four area craftspeople. A one-time employee of the former Dey Bros. department store in Syracuse, Watson said she loves merchandising and enjoys displaying unique pieces of art on shelves and even suspending from ceilings. “We’re creative. We also had top quality plants. We got a reputation of quality plants and unusual products,” she said. It’s tending to her customers and her plants, showcasing the unusual products, and hosting special events to benefit the community that keep her motivated to work six to seven days a week. “I just love it. I love taking care of the plants. I’m one of the few people that really enjoys watering,” she said. And while she has cultivated a solid customer base over the years, she remains savvy in her marketing efforts to draw in new customers. For example, she does a gardening segment on CNY Central television; her active Facebook page has thousands of followers; and she hosts a number of unique events and classes that not only introduce her shop to potential new clients, but some benefit nonprofits as well. About 13 years ago, when the local symphony orchestra was struggling, she said she wanted to help. So, partnering with the group, she began offering concerts, called Soirees, featuring the Syracuse Orchestra’s string quartet. Following up on a suggestion from a customer whose brother is a deejay in retirement, specializing in sock hops, she hosted the greenhouse’s first sock hop. Plants got moved back toward the walls to make room for dancing; a caterer was brought in, offering diner-like food; guests came in period outfits; and proceeds went to the Food Bank of CNY. She is excited about doing it again in October. Via her website, she promotes the greenhouse as an “oasis in the snow,” offering visitors the opportunity to pick up some decorating and gift ideas while taking in the floral and citrus scents. And she also caters to her four-legged friends. Over this past winter, she said, she came up with a “brilliant idea,” although at the time she didn’t appreciate just how brilliant it would become. Feeling it too cold outside to take her dog for a walk, she opted to bring it into the greenhouse. And before long, she was inviting people to bring their dogs into the big greenhouse as well. She looks forward to extending that invitation again this upcoming winter season. For Watson and her customers, the greenhouse is always in season.
“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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