Expanding Explainability: Towards Social Transparency in AI systems

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.

Certain techno-centric pitfalls that are deeply embedded in AI and Computer Science, such as Solutionism (always seeking technical solutions) and Formalism (seeking abstract, mathematical solutions) [32, 87], are likely to further widen these gaps.

On the other hand, implicit in AI systems are human-AI assemblages. Most consequential AI systems are deeply embedded in socio-organizational tapestries in which groups of humans interact with it, going beyond a 1-1 human-AI interaction paradigm. Given this understanding, we might ask: if both AI systems and explanations are socially-situated, then why are we not requiring incorporation of the social aspects when we conceptualize explain-ability in AI systems? How can one form a holistic understanding of an AI system and make informed decisions if one only focuses on the technical half of a sociotechnical system?

If the boundary is traced along the bounds of an algorithm, we risk excluding the human and social factors that significantly impact the way people make sense of a system