# AI for social science and social science of AI: A Survey

arXiv: https://arxiv.org/abs/2401.11839 · HTML: https://mlsanity.com/paper/2401.11839/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 62 of its 148 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 30 of 62)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023)
- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://mlsanity.com/paper/2210.03849/) (2022)
- [Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech](https://mlsanity.com/paper/2302.07736/) (2023)
- [Language Models as Agent Models](https://mlsanity.com/paper/2212.01681/) (2022)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [Agents: An Open-source Framework for Autonomous Language Agents](https://mlsanity.com/paper/2309.07870/) (2023)
- [Who is GPT-3? An Exploration of Personality, Values and Demographics](https://mlsanity.com/paper/2209.14338/) (2022)

## Built on by (most cited first, 29 of 29)

- [Hello Again! LLM-powered Personalized Agent for Long-term Dialogue](https://mlsanity.com/paper/2406.05925/) (2024)
- [Can Large Language Model Agents Simulate Human Trust Behavior?](https://mlsanity.com/paper/2402.04559/) (2024)
- [Knowledge Mechanisms in Large Language Models: A Survey and Perspective](https://mlsanity.com/paper/2407.15017/) (2024)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios](https://mlsanity.com/paper/2412.03920/) (2024)
- [Designing AI-Agents with Personalities: A Psychometric Approach](https://mlsanity.com/paper/2410.19238/) (2024)
- [A Survey of AI Scientists](https://mlsanity.com/paper/2510.23045/) (2025)
- [Preemptive Detection and Correction of Misaligned Actions in LLM Agents](https://mlsanity.com/paper/2407.11843/) (2024)
- [From Prompts to Constructs: A Dual-Validity Framework for Large Language Model Research in Psychology](https://mlsanity.com/paper/2506.16697/) (2025)
- [Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management](https://mlsanity.com/paper/2409.00128/) (2024)
- [Gender Dynamics and Homophily in a Social Network of LLM Agents](https://mlsanity.com/paper/2602.02606/) (2026)
- [AI Agents as Policymakers in Simulated Epidemics](https://mlsanity.com/paper/2601.04245/) (2026)
- [Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science](https://mlsanity.com/paper/2412.09628/) (2024)
- [Dataset Featurization: Uncovering Natural Language Features through Unsupervised Data Reconstruction](https://mlsanity.com/paper/2502.17541/) (2025)
- [Evaluating the Simulation of Human Personality-Driven Susceptibility to Misinformation with LLMs](https://mlsanity.com/paper/2506.23610/) (2025)
- [Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models](https://mlsanity.com/paper/2410.16301/) (2024)
- [Arti-"fickle" Intelligence: Using LLMs as a Tool for Inference in the Political and Social Sciences](https://mlsanity.com/paper/2504.03822/) (2025)
- [Free Lunch for User Experience: Crowdsourcing Agents for Scalable User Studies](https://mlsanity.com/paper/2505.22981/) (2025)
- [Normative Common Ground Replication (NormCoRe): Replication-by-Translation for Studying Norms in Multi-Agent AI](https://mlsanity.com/paper/2603.11974/) (2026)
- [A Target-Aware Analysis of Data Augmentation for Hate Speech Detection](https://mlsanity.com/paper/2410.08053/) (2024)
- [AI-Empowered Human Research Integrating Brain Science and Social Sciences Insights](https://mlsanity.com/paper/2411.12761/) (2024)
- [Random Forest-of-Thoughts: Uncertainty-aware Reasoning for Computational Social Science](https://mlsanity.com/paper/2502.18729/) (2025)
- [e-person Architecture and Framework for Human-AI Co-adventure Relationship](https://mlsanity.com/paper/2503.22181/) (2025)
- [Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities](https://mlsanity.com/paper/2504.06313/) (2025)
- [Epitome: Pioneering an Experimental Platform for AI-Social Science Integration](https://mlsanity.com/paper/2507.01061/) (2025)
- [EduResearchBench: A Hierarchical Atomic Task Decomposition Benchmark for Full-Lifecycle Educational Research](https://mlsanity.com/paper/2602.15034/) (2026)
- [A Simulation-Based Method for Testing Collaborative Learning Scaffolds Using LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2604.11161/) (2026)
- [Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape](https://mlsanity.com/paper/2604.11184/) (2026)
- [BenCSSmark: Making the Social Sciences Count in LLM Research](https://mlsanity.com/paper/2605.04886/) (2026)

## Abstract (arXiv)

Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize previous explorations in the combination of AI and social science into two directions that share common technical approaches but differ in their research objectives. The first direction is focused on AI for social science, where AI is utilized as a powerful tool to enhance various stages of social science research. While the second direction is the social science of AI, which examines AI agents as social entities with their human-like cognitive and linguistic capabilities. By conducting a thorough review, particularly on the substantial progress facilitated by recent advancements in large language models, this paper introduces a fresh perspective to reassess the relationship between AI and social science, provides a cohesive framework that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and also summarized state-of-art experiment simulation platforms to facilitate research in these two directions. We believe that as AI technology continues to advance and intelligent agents find increasing applications in our daily lives, the significance of the combination of AI and social science will become even more prominent.