Expanding Explainability: Towards Social Transparency in AI systems. (arXiv:2101.04719v1 [cs.HC])

As AI-powered systems increasingly mediate consequential decision-making,
their explainability is critical for end-users to take informed and accountable
actions. Explanations in human-human interactions are socially-situated. AI
systems are often socio-organizationally embedded. However, Explainable AI
(XAI) approaches have been predominantly algorithm-centered. We take a
developmental step towards socially-situated XAI by introducing and exploring
Social Transparency (ST), a sociotechnically informed perspective that
incorporates the socio-organizational context into explaining AI-mediated
decision-making. To explore ST conceptually, we conducted interviews with 29 AI
users and practitioners grounded in a speculative design scenario. We suggested
constitutive design elements of ST and developed a conceptual framework to
unpack ST’s effect and implications at the technical, decision-making, and
organizational level. The framework showcases how ST can potentially calibrate
trust in AI, improve decision-making, facilitate organizational collective
actions, and cultivate holistic explainability. Our work contributes to the
discourse of Human-Centered XAI by expanding the design space of XAI.

Source: https://arxiv.org/abs/2101.04719


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