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. A key shortcoming lies in overlooking the fact that AI agents operate within an AI ecosystem composed of multiple interacting agents. When such agents act jointly, misaligned incentives or incompatible technological designs may lead to poor social outcomes. Importantly, this perspective is orthogonal to the ongoing efforts to compare artificial and human behavior. Our argument is not merely conceptual but constitutes a concrete call to action: to establish a systematic research agenda on Artificial Social Intelligence. We illustrate this vision through four complementary research directions: (i) understanding multi-agent alignment in search 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. We conclude by outlining a bold vision: the development of a unified theoretical and empirical framework that supports the investigation of the use cases discussed above, and potentially many more yet to be explored.