The core argument is deceptively simple but far-reaching. AI is transforming how the social sciences observe, model, simulate, and govern human systems, from financial markets to classrooms. Yet at the same time, some of AI’s most stubborn problems—interpretability, controllability, legitimacy, and value alignment—are fundamentally social and institutional questions, not merely technical ones. Why did a model reach a decision? Who is accountable when an autonomous agent acts? What values should an algorithm optimize, and who decides? These are questions that social science has grappled with for centuries, and the authors contend that answering them requires social scientific theory to be built into AI systems from the ground up.

To formalize this two-way relationship, the paper introduces a framework the authors call AI × SS, which unifies two complementary research directions under a single system architecture. The first direction, AI4SS, uses artificial intelligence to advance social science research: machine learning to detect patterns in massive behavioral datasets, natural language processing to analyze text at scale, and simulation to test hypotheses about collective behavior. The second direction, SS4AI, runs the other way, importing insights from sociology, economics, psychology, political science, and law to make AI systems more transparent, governable, and aligned with human values. The multiplication sign in the framework’s name is deliberate: the authors see the combination as producing something qualitatively new rather than a simple sum of the two fields.

A central theoretical pillar of the framework is Parallel Intelligence, or PI, an approach associated with Fei-Yue Wang’s long-running research program on complex systems. In the PI paradigm, real social systems are paired with artificial, computational counterparts—virtual societies, synthetic populations, and simulated institutions—that run in parallel with reality. Data flows between the real and artificial systems, allowing researchers to conduct experiments that would be impossible or unethical in the real world, to predict the consequences of policy interventions before deploying them, and to prescribe better decisions by learning from the parallel worlds. Generative AI and foundation models have dramatically strengthened this vision, because large models can now generate plausible synthetic agents, scenarios, and social dynamics at unprecedented scale and fidelity.

The review organizes the current research landscape into four methodological pathways. The first is data representation: how human behavior, institutions, and social relations are encoded so that machines can process them, ranging from network representations of societies to multimodal embeddings of text, images, and interaction traces. The second is analysis and modeling, where techniques such as graph learning, causal inference, and large language model-based agents are used to extract structure and explanation from social data. The third is simulation and experimentation, encompassing agent-based modeling, generative agent societies, and the construction of computational laboratories for testing social theories. The fourth is decision and optimization, where models are turned into actionable guidance for governance, resource allocation, and policy design under uncertainty.

Along each of these pathways, the authors survey representative studies that illustrate how the frontier is moving. In data representation, the explosion of digital trace data—from social media, mobility records, and transaction logs—has created both opportunity and risk, demanding new methods for privacy preservation and bias correction. In analysis and modeling, foundation models pretrained on vast corpora are being repurposed as social simulators, capable of reproducing survey responses, opinion dynamics, and market behavior, though the authors caution that their fidelity and faithfulness remain open empirical questions. In simulation, generative agents that plan, remember, and interact are enabling synthetic societies in which researchers can stress-test interventions against thousands of virtual lives before touching the real world.

The paper then grounds these abstractions in concrete domains. In finance, AI-driven models now read markets, news flows, and investor sentiment in real time, while social science supplies the institutional understanding of regulation, trust, and systemic risk. In communication, recommendation algorithms and large language models are reshaping public discourse, and computational social science is racing to measure effects such as polarization and misinformation. In management, agentic AI systems are beginning to participate in organizational decision-making, raising questions about delegation, oversight, and human–machine collaboration that draw directly on organizational theory. In art, generative models are transforming creative production and raising questions of authorship and value. In education, adaptive AI tutors promise personalization at scale, but their legitimacy and fairness depend on pedagogical and ethical frameworks that only the social sciences can supply.

What emerges across all of these domains, the authors argue, is a new research structure rather than a loose collection of applications. Institutions, governance mechanisms, and human–machine collaboration are not afterthoughts bolted onto AI systems; they are constitutive components of any AI that operates in society. An autonomous agent acting in a market, a classroom, or a city is embedded in norms, incentives, and legal regimes, and its behavior cannot be predicted or controlled without modeling those social layers. Conversely, the arrival of agentic AI—systems that pursue goals, coordinate with other agents, and act over long horizons—creates genuinely novel social phenomena, such as economies of interacting algorithms, that demand new social scientific theory in their own right.

The review also reflects an unusually broad collaborative effort. The author team spans institutions including Macau University of Science and Technology, the Chinese Academy of Sciences, Beijing Jiaotong University, Tianjin University, the University of Science and Technology of China, Zhejiang Lab, Beijing Institute of Technology, the University of Chinese Academy of Sciences, and the University of Glasgow, and grew out of a series of workshops organized under the Artificial Intelligence for Autonomous Science initiative between 2023 and 2026, covering topics from social transportation to intelligent art and parallel decision theaters. That breadth mirrors the paper’s central claim: no single discipline owns the AI–society problem anymore.

For researchers and policymakers alike, the practical message is that the next generation of breakthroughs will come from teams that treat AI and the social sciences as a single coupled system. Building interpretable models requires theories of explanation that humans actually find meaningful. Building controllable agents requires institutional design, incentive analysis, and accountability structures. Building value-aligned systems requires explicit, contestable accounts of whose values are being encoded. The AI × SS framework offers a map of that territory, organizing data, modeling, simulation, and decision-making into a coherent architecture in which artificial and human systems learn from each other continuously. If the authors are right, the most important new social science of the coming decade may be the one that is co-written with machines.

Subject of Research: The bidirectional co-evolution of artificial intelligence and the social sciences through a unified AI × SS framework based on parallel intelligence

Article Title: Ai and social sciences: new AI, new studies, and new social sciences

Article References: Ni, Q., Lin, F., Zhang, H., Xue, X., Zhang, T., Lü, L., Zhang, B., Lu, Y., Li, D., Yang, L., Ye, P., Li, X., Guo, C., Huang, J., Zheng, X., Yu, H., & Wang, F.-Y. (2026). Ai and social sciences: new AI, new studies, and new social sciences. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11693-5

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11693-5

Keywords: artificial intelligence, social sciences, parallel intelligence, AI4SS, SS4AI, computational social science, agentic AI, generative AI, foundation models, value alignment, human-machine collaboration, AI governance