Does Explainable AI Have Moral Value?
This work addresses the ethical and practical challenges of implementing XAI for stakeholders affected by AI systems, but it is incremental as it synthesizes existing views without introducing new technical methods.
The paper tackles the problem of fragmented discourse on Explainable AI (XAI) by proposing a unifying ethical framework based on moral duties and reciprocity, arguing that XAI sustains symmetry and agency in AI decision-making, and it assesses leading XAI communities to reveal gaps between ideal reciprocity and practical feasibility.
Explainable AI (XAI) aims to bridge the gap between complex algorithmic systems and human stakeholders. Current discourse often examines XAI in isolation as either a technological tool, user interface, or policy mechanism. This paper proposes a unifying ethical framework grounded in moral duties and the concept of reciprocity. We argue that XAI should be appreciated not merely as a right, but as part of our moral duties that helps sustain a reciprocal relationship between humans affected by AI systems. This is because, we argue, explanations help sustain constitutive symmetry and agency in AI-led decision-making processes. We then assess leading XAI communities and reveal gaps between the ideal of reciprocity and practical feasibility. Machine learning offers useful techniques but overlooks evaluation and adoption challenges. Human-computer interaction provides preliminary insights but oversimplifies organizational contexts. Policies espouse accountability but lack technical nuance. Synthesizing these views exposes barriers to implementable, ethical XAI. Still, positioning XAI as a moral duty transcends rights-based discourse to capture a more robust and complete moral picture. This paper provides an accessible, detailed analysis elucidating the moral value of explainability.