Social Science

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Social science is the systematic study of how people behave in groups and societies, spanning economics, political science, sociology, psychology, anthropology, communication, and demography. For most of its history the field relied on surveys, interviews, archives, and carefully designed experiments, all of which are slow and expensive to scale. Machine learning and, more recently, large language models (LLMs) have changed that calculus by making it possible to read millions of documents, classify open-ended answers in seconds, and even generate simulated research participants. The result is a two-way relationship that now defines the area: AI has become a powerful research instrument for social scientists, and AI systems themselves have become one of the most consequential subjects social scientists study.

The first direction, usually called computational social science, uses data science and AI to measure behavior at a scale that was previously impossible. It includes natural language processing of speeches and social media posts, social network analysis of communication patterns, and predictive models built from large administrative datasets. The second direction asks how AI changes the societies it enters: whether chatbots can change political opinions, whether feed algorithms deepen polarization, how AI companions affect loneliness, and how automation reshapes work. A third, quieter link runs the other way, because modern AI depends on social science ideas, from the human preference data used to align models to the survey methods used to evaluate them.

What makes the current moment different is that generative AI now touches nearly every step of the research process. Researchers use LLMs to draft survey questions, code qualitative data, run conversational experiments, simulate respondents, and predict the outcomes of studies before running them. Each of these uses has shown real promise in peer-reviewed work, and each carries risks: models reflect a narrow slice of humanity, their outputs shift with prompts and versions, and the same tools that help researchers can contaminate the online panels that research depends on. This article covers both sides, distinguishing what has been demonstrated from what remains preliminary or contested.


AI is becoming both a powerful new instrument for studying society and one of the most important social forces that social scientists now need to study.


From Computational Social Science to Generative AI

The modern field took shape in 2009, when David Lazer and fourteen coauthors published "Computational Social Science" in Science, arguing that the digital traces people leave through phone calls, email, and online activity, combined with growing computing power, could reveal patterns of individual and group behavior at unprecedented scale. Early work leaned on network analysis and on text-mining methods such as topic models like latent Dirichlet allocation, sentiment analysis, and word embeddings that represent meaning as geometry. Training programs such as the Summer Institutes in Computational Social Science, co-organized by Christopher Bail and Matthew Salganik since 2017, spread these methods to a generation of researchers working in Python, R, and Jupyter notebooks. By the late 2010s, computational methods had become a familiar part of political science, sociology, and communication research.

A recurring lesson from this period is that prediction and explanation are different goals. Machine learning excels at prediction, while social science has traditionally prioritized causal explanation, a distinction at the heart of debates over causation and correlation. The Fragile Families Challenge, published in PNAS in 2020 with 112 coauthors, tested how far prediction can go: 160 teams used rich longitudinal data on more than 4,000 families, collected from a child's birth to age 9, to predict six outcomes at age 15, such as a child's grade point average and whether a family would be evicted. Even the best models were not very accurate, simpler models often outperformed complex ones, and the organizers warned that policymakers should not assume sophisticated models will produce accurate predictions about an individual's future. Because the challenge used the common task method, a design borrowed from machine learning competitions in which every team predicts the same outcomes from the same data, its disappointing results cannot be dismissed as the failure of any one team or technique.

Generative AI arrived on top of this foundation. In 2023, Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli at the University of Zurich showed in PNAS that ChatGPT, given the same codebooks used to instruct research assistants, outperformed crowd workers on Amazon Mechanical Turk by about 25 percentage points in accuracy on tasks such as relevance, stance, topic, and frame detection, at roughly one thirtieth of the cost. The following year, Duke sociologist Christopher Bail argued in PNAS that generative AI could improve survey research, online experiments, automated content analysis, and agent-based models, while cataloguing its limitations and urging social scientists to build open-source research infrastructure. Together, these papers mark the shift from AI as a specialized text-mining tool to AI as a general-purpose research collaborator.

AI as a Research Instrument

Text as Data and Automated Annotation

The most mature use of LLMs in social science is turning unstructured text into measurable variables. Researchers who once spent months hand-coding protest reports, legislative speeches, or open-ended survey answers can now use carefully designed prompts and few-shot examples to classify thousands of documents in an afternoon, including in languages that were previously too costly to code. Gilardi's team found that ChatGPT's intercoder agreement exceeded that of both crowd workers and trained annotators, and a follow-up study by the same group showed that open-source models also outperformed crowd workers on most tasks. That second result matters because open models can be archived and rerun, which proprietary services cannot guarantee.

Good practice treats the model as a measurement instrument that must be validated rather than trusted. Researchers typically hand-code a gold-standard sample, compare model labels against it, report agreement statistics, and keep humans in the loop for ambiguous cases. Configuration choices matter as well: Gilardi's group found that a lower temperature setting increased consistency without reducing accuracy, and careful studies now record the exact model version, prompt, and settings so others can reproduce their measurements.

Silicon Samples and Simulated Respondents

A more ambitious idea is to ask a language model to answer surveys as if it were a particular person. In a 2023 Political Analysis paper, Lisa Argyle and colleagues at Brigham Young University conditioned GPT-3 on thousands of first-person demographic backstories drawn from real participants in large U.S. surveys, creating what they called "silicon samples." They argued that the model's biases are fine-grained and demographically correlated rather than uniform, so careful conditioning can reproduce the response patterns of many different subgroups, a property they named algorithmic fidelity. If it holds, silicon sampling could make pilot testing far cheaper and let researchers explore questions before fielding expensive surveys.

The critique arrived quickly. James Bisbee and colleagues at Vanderbilt reported in Political Analysis in 2024 that synthetic ChatGPT opinions looked remarkably similar to American National Election Studies respondents at the level of averages, but that regressing opinions on demographic attributes, the bread-and-butter analysis of public opinion research, frequently led to wrong conclusions. They also found synthetic responses less variable than real ones, sensitive to prompt wording and model version, and unable to meet basic requirements for replication. Follow-up analyses describe LLM-generated populations as behaving like samples with smaller effective sizes than the human groups they imitate, so silicon samples are best treated as tools for generating hypotheses and stress-testing questionnaires rather than as replacements for people.

Generative Agents and Social Simulation

Agent-based modeling has long been used to study how individual rules produce collective patterns such as segregation, market swings, or the spread of norms, but traditional agents follow simple rules written by researchers. Generative agents replace those rules with language models. In 2023, Joon Sung Park and colleagues at Stanford and Google populated a small virtual town with 25 AI agents that could remember experiences, reflect on them, and plan their days, producing believable social behavior such as spreading word of a party and coordinating to attend it. The architecture's memory, reflection, and planning components became a template for later simulations of social life, and its ability to produce group behavior no one scripted is an example of emergence.

The team's 2024 follow-up, "Generative Agent Simulations of 1,000 People," shifted the goal from believability to accuracy. The researchers interviewed 1,052 U.S. adults with an AI interviewer, then gave each full interview transcript to a language model instructed to imitate that person. The resulting agents replicated participants' answers on the General Social Survey 85% as accurately as participants replicated their own answers two weeks later, performed comparably on personality and economic-game measures, and showed smaller accuracy gaps across racial and ideological groups than agents built from demographic descriptions alone. Because the agents are grounded in real people's life stories, the team made them available to other researchers only through a review process designed to protect participants' privacy.

Simulation remains a contested frontier. Supporters see agent populations as testbeds for piloting policies, messages, and interventions before exposing real people to them, and Park has since co-founded Simile, a company building foundation models of human behavior for that purpose. Critics note that a simulated population can match averages while missing the variance, minority viewpoints, and surprises that make social research valuable, and that interactions among agents built on the same underlying model may amplify that model's quirks. For now, the strongest evidence supports simulations as complements to human studies, used to narrow down which experiments are worth running.

Predicting Experiments and Human Behavior

A related line of work asks whether LLMs can forecast the results of experiments before anyone runs them. In a study published in Nature in July 2026, Ashwini Ashokkumar, Luke Hewitt, Isaias Ghezae, and Robb Willer built an archive of 70 preregistered, nationally representative U.S. survey experiments involving 469 experimental effects and 119,330 participants, then prompted GPT-4 to simulate how representative samples of Americans would respond to each study's materials. The simulated treatment effects correlated strongly with the real ones, matching the accuracy of pooled human forecasters, and the correlation stayed high both for studies published after the model's training cutoff and for prominent open-weight models. The predictions also systematically overestimated effect sizes, and in a second archive of 15 megastudies with 606 effects, correlations were lower, though still comparable to those of pooled expert forecasters.

Psychology has pursued an even broader goal. Marcel Binz, Eric Schulz, and colleagues at Helmholtz Munich fine-tuned Meta's Llama 3.1 70B model on Psych-101, a dataset of more than 10 million choices made by over 60,000 participants in 160 psychology experiments, and published the resulting model, Centaur, in Nature in 2025. Centaur predicted the behavior of held-out participants better than the specialized cognitive models built for individual tasks in nearly every comparison, generalized to modified and entirely new tasks, and developed internal representations that aligned more closely with human brain activity measured by fMRI. The authors also used Centaur to guide the design of new cognitive models that are both predictive and interpretable, showing how a black-box predictor can still support theory building.

These results have drawn pointed methodological critiques. Skeptics question what exactly is being predicted when a model reproduces choices in laboratory tasks, and they note that Psych-101 draws mainly on educated, industrialized Western participants. The Centaur team has acknowledged the need for external validation and released the model openly so the research community can test it. Taken together, the experiment-prediction studies suggest that LLMs are most useful today for pilot testing materials, prioritizing which interventions to study, and flagging results that may deserve replication.

AI as a Subject of Social Science

Persuasion, Misinformation, and Democracy

Some of the most consequential recent experiments test whether conversational AI can change what people believe, including beliefs rooted in misinformation. In a 2024 Science paper, Thomas Costello, Gordon Pennycook, and David Rand had 2,190 Americans who endorsed a conspiracy theory describe it in their own words, then hold a short, personalized, evidence-based conversation with GPT-4 Turbo. Belief in the chosen conspiracy fell by about 20% on average, roughly one in four participants disavowed it entirely, the effect persisted two months later, and belief in unrelated conspiracies declined as well. The authors argue this challenges the view that conspiracy beliefs mainly satisfy psychological needs and resist evidence; many believers, they suggest, had simply never encountered counterarguments tailored to their specific claims.

The same capability raises obvious concerns about political influence. A 2024 PNAS study by Kobi Hackenburg and Helen Margetts at Oxford tested whether GPT-4 messages tailored to individuals' demographic and political profiles were more persuasive than generic ones; across 8,587 participants, microtargeted messages were not significantly more persuasive than untargeted ones, suggesting the power lies in persuasive content rather than personalization. In December 2025, Hackenburg and colleagues reported in Science on three experiments with 76,977 UK participants, 19 language models, and 707 political issues, finding that post-training and prompting techniques raised persuasiveness more than model size did, largely by packing arguments with information, and that the techniques that increased persuasion also systematically decreased factual accuracy. A companion study by Lin and colleagues in Nature found that conversations with chatbots shifted voters' attitudes toward candidates and policies in the United States, Canada, and Poland.

These findings revisit an older controversy. In 2016, Alexander Nix, then CEO of Cambridge Analytica, publicly promoted "psychographic" targeting that matched political messages to personality profiles, and in 2018 it emerged that the firm had obtained data on up to 87 million Facebook users through a quiz app; days later, the firm suspended Nix after he was filmed boasting about its tactics. Advertising experiments by Michal Kosinski and colleagues have shown that personality-matched ads can outperform generic ones, yet political operatives and analysts have questioned whether Cambridge Analytica's methods were measurably effective in elections. The newer LLM research suggests the main risk may have shifted from precise psychological profiling to cheap, scalable, information-dense persuasion, which has implications for privacy law, election regulation, and AI governance.

Deliberation and Common Ground

AI can also be designed to help people disagree more productively. Google DeepMind's "Habermas Machine," described in Science in October 2024 by Michael Henry Tessler and colleagues, pairs a language model that drafts group statements summarizing participants' views with a personalized reward model that predicts how much each participant will agree with each draft. In experiments with 5,734 UK participants, including a demographically representative group recruited through the Sortition Foundation, participants preferred the AI's statements over those written by human mediators 56% of the time, rated them as clearer and more informative, and ended discussions less divided on issues such as Brexit, immigration, and climate change. The system also incorporated minority critiques into revised statements rather than simply echoing the majority.

Critics caution that maximizing agreement is not the same as deliberation. Philosopher Henrik Skaug Sætra argued that machine-mediated exchanges among isolated participants risk becoming a "consensus machine" that optimizes language rather than fostering direct engagement with conflicting values. The designers themselves note that the system does not fact-check, keep discussions on topic, or moderate discourse, all functions that skilled human facilitators provide. AI mediators may therefore be most valuable as aids to human-led processes, such as summarizing thousands of public comments for citizens' assemblies or government consultations, rather than as replacements for human facilitators.

Feeds, Algorithms, and Polarization

Social media recommendation algorithms are among the most widely deployed AI systems, and social scientists have spent years debating their political effects. In 2023, a collaboration between independent academics and Meta published the first results of the U.S. 2020 Facebook and Instagram Election Study in three Science papers and one Nature paper. In one experiment led by Andrew Guess, switching consenting users from algorithmic to reverse-chronological feeds for three months sharply reduced the time they spent on the platforms and increased their exposure to political and untrustworthy content, yet did not significantly change issue polarization, affective polarization, or political knowledge. Related experiments that removed reshared content or reduced exposure to like-minded sources likewise found no detectable effect on polarization.

A 2025 study reached a different conclusion with a different design. Tiziano Piccardi, Chenyan Jia, and colleagues at Stanford, the University of Washington, and Northeastern built a browser extension that used an LLM to score and rerank posts on X in real time, without the platform's cooperation, and randomly assigned 1,256 consenting users during the 2024 presidential campaign to see more or less content expressing antidemocratic attitudes and partisan animosity. Over ten days, changing that exposure shifted feelings toward the opposing party by more than two points on a 100-point thermometer, with no detectable difference between partisans, although the sample was limited to people who used X in a web browser. One reading of the contrast with the Meta studies is that targeting specific hostile content matters more than changing a feed's overall logic, and the method itself lets researchers run causal experiments that once required platform permission.

Companionship and Wellbeing

As chatbots become conversational partners, researchers are measuring their effects on loneliness and social life. In March 2025, OpenAI and the MIT Media Lab released two parallel studies: an automated analysis of close to 40 million ChatGPT interactions paired with surveys of 4,076 users, and a four-week randomized controlled trial with 981 participants assigned to different text and voice conditions. Across conditions, higher daily usage correlated with greater loneliness, emotional dependence, and problematic use and with less socializing with other people, and the early benefits of voice conversations faded at heavy usage. The researchers stress that these relationships are correlational, so it remains unclear whether heavy use deepens isolation or whether lonely people gravitate toward heavy use.

The question matters because dedicated AI companion apps such as Replika and Character.AI are built around emotional relationships rather than tasks. Social scientists are now asking how these relationships interact with existing friendships and families, which design choices encourage healthy rather than dependent use, and how to protect vulnerable users, questions that connect AI research to mental health. Research on emotionally aware AI provides tools for this work, but it also raises concerns about manipulation when systems that can read and respond to emotion are optimized for engagement.

Work, Learning, and Collaboration

Economists and sociologists are also tracking how AI changes work, a topic covered in depth on Help Wanted. Task-based research finds that generative AI reaches into writing, analysis, and coding, kinds of work that earlier waves of automation left largely untouched, and early payroll evidence points to weaker hiring of young workers in highly exposed occupations rather than broad job loss. For human resources leaders, these findings shift attention from replacing workers to redesigning tasks, training, and career ladders. Social scientists contribute the methods needed to separate genuine effects from hype, including randomized trials, administrative data, and careful measurement of what workers actually do.

Education and everyday collaboration are changing in parallel. The shift was already visible in the title of Tom Vander Ark's 2018 Forbes column, "Why Social Studies Is Becoming AI Studies," and today the conversation has expanded to how students and teachers use AI tools themselves (see Education). Remote work offers another example: Microsoft's Teams "Together Mode," introduced in 2020, placed video-call participants in a shared virtual space in an effort to make online meetings feel more like being in the same room. Questions about whether such designs improve attention, belonging, and learning are, at bottom, social science questions.

Limits, Risks, and Research Integrity

LLMs learn from text that overrepresents some people. A 2023 analysis by Mohammad Atari, Joseph Henrich, and colleagues at Harvard found that LLM responses to psychological measures most resembled those of people from Western, educated, industrialized, rich, and democratic (WEIRD) societies, and that this resemblance declined sharply with cultural distance from the United States, with a correlation of -0.70. That bias matters for any study that uses models as stand-ins for people, and it may compound when alignment training narrows the range of opinions a model expresses. Careful researchers therefore test models against cross-cultural data and report which populations a simulation can and cannot represent, an issue that also affects generalization to groups underrepresented in training data.

AI also threatens the infrastructure of social research. In a 2025 PNAS paper, Dartmouth political scientist Sean Westwood built an autonomous "synthetic respondent" that completed online surveys while maintaining a coherent demographic persona and passed 99.8% of 6,000 standard attention-check trials, along with logic puzzles and "reverse shibboleth" questions designed to catch machines. Westwood warned that such agents could cheaply skew polls and studies, and the paper received the journal's 2025 Cozzarelli Prize. Some survey-industry researchers counter that the study demonstrates technical feasibility rather than documented large-scale fraud and that platform-level defenses are harder to defeat than a single questionnaire. Either way, data quality checks designed to catch inattentive humans are no longer sufficient on their own.

Ethics review is also struggling to keep pace. Between November 2024 and March 2025, researchers affiliated with the University of Zurich secretly posted more than 1,700 AI-generated comments, some containing fabricated personal stories, on Reddit's r/changemyview forum to test AI persuasion without users' knowledge. After moderators disclosed the experiment, Reddit banned the accounts and sent formal legal demands, the university's ethics commission issued a formal warning to the lead researcher, and the episode became a widely cited example of why consent and platform rules apply to AI experiments. By contrast, the persuasion and conspiracy studies described above relied on consenting participants recruited specifically for research, showing that rigorous work on AI influence does not require deceiving the public.

The field is responding with shared standards. GUIDE-LLM, a 14-item reporting checklist published in Nature Human Behaviour in June 2026 by Stefan Feuerriegel and dozens of coauthors, was developed through a Delphi consensus process with about 80 experts and asks researchers to report which model and version they used, their prompts and settings, and how they validated model outputs. The authors point out that a label like "ChatGPT" can refer to several underlying models, each with dated versions, so studies that omit these details cannot be reproduced. Combined with preregistration, open code, and validation against human data, such checklists aim to make LLM-based findings as auditable as any other form of evaluation, and they align with broader ethics and data governance practice.

Why This Matters

The relationship between AI and social science is becoming one of mutual dependence. AI gives social scientists faster measurement, cheaper pilots, and new kinds of experiments, while social science gives developers and policymakers the evidence they need to judge whether AI systems persuade, polarize, isolate, or empower people. Questions that once relied on speculation, such as whether chatbots can change votes or whether feed algorithms drive division, now have randomized evidence behind them, even if the answers are more nuanced than headlines suggest. That evidence feeds directly into AI governance, election law, platform design, and regulation.

The same tools also force social scientists to rethink basic assumptions. If a coherent survey answer no longer proves a human wrote it, if a model can forecast an experiment's result, and if a simulated population can be generated in minutes, the field must decide what counts as evidence about people. The most defensible answer emerging from the research is a hybrid one: use AI to scale measurement and generate hypotheses, then test conclusions with real people, transparent methods, and attention to which humans a model actually represents. Philosopher Brian Epstein has warned that without significant changes the social sciences risk becoming irrelevant, and AI is now one of the strongest pressures pushing the field to change.

Recent Developments

Research from 2025 and 2026 has moved several early claims from preprints into peer-reviewed journals. The experiment-prediction study by Ashokkumar, Hewitt, Ghezae, and Willer appeared in Nature in July 2026, Centaur appeared in Nature in 2025, and the persuasion studies by Hackenburg and colleagues and by Lin and colleagues appeared in Science and Nature in December 2025, with Hackenburg's persuasion research cited in the International AI Safety Report 2026. The Stanford simulation project was updated in June 2026 under a new title emphasizing LLM agents grounded in people's self-reports. Researchers have also begun describing this territory as "AI behavioral science," a field that covers how AI can enhance behavioral research, how behavioral science can improve AI design, and how society changes as humans and AI interact.

Methodological safeguards are maturing alongside the findings. The GUIDE-LLM checklist offers a common reporting standard, Westwood's prize-winning survey study has pushed researchers and panel providers to test their bot defenses against modern AI agents, and debates over Centaur and silicon sampling have produced more careful benchmarks of when simulations work and when they fail. Important open questions remain: whether persuasion effects measured in experiments carry over to real campaigns, how AI companions affect long-term wellbeing, whether simulations can represent non-Western and minority populations, and how to run field experiments on platforms ethically. These are social science questions as much as technical ones, which is why social scientists are likely to play a growing role in shaping how AI is built and governed.

Additional Viewing

Can Generative AI Improve Social Science? | Christopher Bail, Duke University

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Duke sociologist Christopher Bail presents the case from his 2024 PNAS article that generative AI can improve survey research, online experiments, content analysis, and agent-based modeling, along with the limitations researchers must manage. The talk is a natural companion to the "From Computational Social Science to Generative AI" section and to the discussion of open-source research infrastructure.

Measuring the Predictability of Life Outcomes | Matthew Salganik - Princeton CITP Lunch Seminar

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Princeton sociologist Matthew Salganik discusses the Fragile Families Challenge, in which 160 teams tried to predict life outcomes from rich longitudinal survey data. His explanation of the common task method and of why sophisticated models still struggled grounds the prediction-versus-explanation discussion in the history section.

Generative Agents: Interactive Simulacra of Human Behavior | Joon Sung Park, Stanford

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Stanford researcher Joon Sung Park presents the generative agents architecture of memory, reflection, and planning behind the 25-agent virtual town described in the "Generative Agents and Social Simulation" section. It explains the design choices that his later simulation of more than 1,000 real people built on.

Durably Reducing Conspiracy Beliefs Through Dialogues with AI | David G. Rand

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David Rand presents the Science study in which personalized conversations with GPT-4 Turbo durably reduced belief in conspiracy theories. It is the most direct companion to the "Persuasion, Misinformation, and Democracy" section and shows how an LLM can serve as both an experimental intervention and a research instrument.

AI Can Help Humans Find Common Ground in Democratic Deliberation | Michael Henry Tessler - IASEAI '25

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In this IASEAI '25 session, Michael Henry Tessler presents the Habermas Machine research on AI-mediated deliberation. It shows how a statement generator and a personalized reward model produced group statements that participants preferred over those of human mediators, supporting the "Deliberation and Common Ground" section.

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A first look at Microsoft Teams' then-forthcoming "Together" mode, the virtual collaboration feature described in the Microsoft resource linked at the top of this page. It illustrates the shared-space meeting design discussed in the "Work, Learning, and Collaboration" section.

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"Why TikTok is Now the No. 1 Social Media App" traces TikTok's rapid rise, its parent company ByteDance, and the controversies surrounding the platform. TikTok's recommendation feed is a prominent example of the algorithmic ranking systems examined in the "Feeds, Algorithms, and Polarization" section.

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In this 2016 Concordia Annual Summit presentation, "The Power of Big Data and Psychographics," Cambridge Analytica CEO Alexander Nix promotes big data and personality-based targeting in elections. Watch it alongside the "Persuasion, Misinformation, and Democracy" section, which covers the 2018 data scandal, doubts about psychographic targeting's effectiveness, and newer evidence on AI microtargeting.

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Duncan Watts reviews the progress and open challenges of computational social science in "Computational Social Science: Progress & Future Challenges," a talk from DataEngConf. It provides background for the "From Computational Social Science to Generative AI" section.

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"Social and Emotional Artificial Intelligence" examines how artificial agents can be given the ability to reason about affect, a key element of human interaction. It offers technical background for the "Companionship and Wellbeing" section and its questions about emotionally engaging AI.

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"Keynote: Machine Learning for Social Science" introduces computational social science and explains what sets machine learning for social science apart from other uses of the technology. It complements the history section's discussion of prediction versus explanation.

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In "The End of Social Science as We Know it," philosopher Brian Epstein warns that the social sciences risk becoming irrelevant and obsolete without significant changes. His argument frames the "Why This Matters" discussion of how AI is forcing the field to rethink what counts as evidence about people.

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This introductory lecture from the 2019 Summer Institute in Computational Social Science, the training program co-organized by Christopher Bail and Matthew Salganik, surveys the field's core ideas and methods. It is a practical starting point for readers who want to learn the techniques described in the history section.

References

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