Artificial Social Intelligence

Abstract: Artificial intelligence (AI) has been growing at an unprecedented pace. Many of us have experienced a “ChatGPT moment” — a realization that AI will profoundly transform our lives. While numerous challenges and calls for improvement remain, there is little doubt that AI agents will play a central role in shaping our future. We argue, however, that the prevailing perspective on AI agent design is insufficient for achieving desirable social welfare, not merely due to computational or regulatory constraints. While it is understood that AI agents should be orchestrated in order to be used by an organization, and that system-level outcomes depend not only on the design of individual agents, the far more intricate reality is that the combination of misaligned incentives and incompatible technological designs may lead to poor social outcomes.  Our argument is not merely conceptual but constitutes a concrete call to action: to establish a systematic research agenda on Artificial Social Intelligence, tackling multi-agent alignment among incentive-wise and technology-wise diverse AI agents. We illustrate this vision through four complementary research directions: (i) understanding multi-agent alignment in information retrieval (search, RAG, attribution) ecosystems, (ii) analyzing model selection in language-based economics as a strategic choice, (iii) rethinking fairness and regulation through the lens of multi-agent ethics, and (iv) designing hybrid social laws for human–AI coexistence. Together, these directions outline a roadmap toward welfare-maximizing AI societies—an essential step toward socially aligned intelligence.