Mathematical decisions and non-causal elements of explainable AI

The social implications of algorithmic decision-making in sensitive contexts have generated lively debates among multiple stakeholders, suc h as moral and political philosophers, computer scientists, and the public. Yet, the lack of a common language and a conceptual framework for an appropriate bridging of the mor al, technical, and political aspects of the debate prevents the discussion to be as effective a s it can be. Social scientists and psychologists are contributing to this debate by gather ing a wealth of empirical data, yet a philosophical analysis of the social implications of a lgorithmic decision-making remains comparatively impoverished. In attempting to address this lacuna, this paper argues that a hierarchy of different types of explanations for why and how an algorithmic decision outcome is achieved can establish the relevant connection between t he moral and technical aspects of algorithmic decision-making. In particular, I offer a multifaceted conceptual framework for the explanations and the interpretations of algorithmic de cisions, and I claim that this framework can lay the groundwork for a focused discussion among mu ltiple stakeholders about the social implications of algorithmic decision-making, as we ll as AI governance and ethics more generally.