Socially-Aware Explanations: Integrating Peer and Community Perspectives

Current XAI research, as summarized by Rong et al. (2022) and many survey papers, focuses primarily on the individual user’s experience, but Ding et al. (2024) and others highlight the importance of social context (e.g., peer influence, KOLs). This idea proposes integrating community-driven explanations: for instance, surfacing how others with similar backgrounds or goals interpreted a model’s output, or showing consensus (or dissent) among a group. In collaborative work or consumer settings, users could see anonymized “explanation snippets” or ratings from trusted peers, blending AI rationales with social proof. This “social layer” draws on social psychology and HCI principles, providing a richer and potentially more persuasive explanation context. It could also help calibrate trust—if an explanation is controversial among peers, the user may scrutinize it more carefully. This represents a novel synthesis of social computing and XAI, opening the door to community-driven interpretability.

References:

1. Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations. Yao Rong, Tobias Leemann, Thai-trang Nguyen, Lisa Fiedler, Peizhu Qian, Vaibhav Unhelkar, Tina Seidel, Gjergji Kasneci, Enkelejda Kasneci (2022). IEEE Transactions on Pattern Analysis and Machine Intelligence.
2. Unveiling user responses to AI-powered personalised recommendations: a qualitative study of consumer engagement dynamics on Douyin. Liang Ding, G. Antonucci, M. Venditti (2024). Qualitative Market Research.