  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.
