Expanding Explainability: Towards Social Transparency in AI systems

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.

Accepted to CHI2021

References in corpus (9)

• Towards A Rigorous Science of Interpretable Machine Learning
• Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
• Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
• Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
• Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
• Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes
• Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
• Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
• Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study

Cited by in corpus (2)

• Situated Case Studies for a Human-Centered Design of Explanation User Interfaces
• LEx: A Framework for Operationalising Layers of Machine Learning Explanations