Expanding Explainability: Towards Social Transparency in AI systems

• URL: http://arxiv.org/abs/2101.04719v1
• Date: Tue, 12 Jan 2021 19:44:27 GMT
• Title: Expanding Explainability: Towards Social Transparency in AI systems
• Authors: Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, Justin D. Weisz
• Abstract summary: Social Transparency (ST) is a socio-technically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. Our work contributes to the discourse of Human-Centered XAI by expanding the design space of XAI.
• Score: 20.41177660318785
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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