Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions

TL;DR

A set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI are identified in the context of social intelligence concepts and prior progress in Social-AI research.

Abstract

Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial). Progress towards Social-AI has accelerated in the past decade across several computing communities, including natural language processing, machine learning, robotics, human-machine interaction, computer vision, and speech. Natural language processing, in particular, has been prominent in Social-AI research, as language plays a key role in constructing the social world. In this position paper, we identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI. We anchor our discussion in the context of social intelligence concepts and prior progress in Social-AI research.

Figures (2)

• Figure 1: (A) Four core technical challenges in Social-AI research, illustrated in an example context of a Social-AI agent observing and learning from a human-human interaction. (B) Social contexts in which Social-AI agents can be situated, with interactions spanning social units, interaction structures, and timescales. Interactions can span social settings, degrees of agent embodiment, and social attributes of humans, with agents in several roles.
• Figure 2: Cumulative number of Social-AI papers over time, based on the 3,257 papers from our Semantic Scholar Social-AI queries. Interest in Social-AI research has been accelerating across computing communities.