This position paper identifies four core technical challenges and open questions for advancing social intelligence in AI agents (Social-AI):
(C1) Ambiguity in Constructs: Social constructs have inherent ambiguity in their definition and interpretation. Researchers must explore methods to represent this ambiguity, such as using flexible natural language label spaces instead of predefined static labels.
(C2) Nuanced Signals: Social signals can be highly nuanced, with small changes leading to large shifts in meaning. Advancing the ability to process fine-grained multimodal social signals, including recognizing the absence of cues, is an open challenge.
(C3) Multiple Perspectives: Actors in social interactions bring their own evolving perspectives, experiences, and roles, which can interdependently influence each other. Developing models that can reason over these dynamic, concurrent multiple perspectives is crucial.
(C4) Agency and Adaptation: Social-AI agents must be goal-oriented, learning from both explicit and implicit social signals to adapt their behavior. Researchers must create mechanisms for agents to estimate success in achieving social goals and build shared social memory with other actors.
Addressing these challenges will require advances across computing communities, including natural language processing, machine learning, robotics, human-computer interaction, computer vision, and speech. Participatory AI frameworks, mitigating social biases, and preserving user privacy are also important considerations for developing ethical and trustworthy Social-AI systems.
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by Leena Mathur... pada arxiv.org 04-18-2024
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