Evaluating Explainability Metrics: Ethical Implications of What We Choose to Explain

Abstract

Explainable Artificial Intelligence (XAI) has emerged as a critical component in ethical and responsible AI deployment, allowing humans to interpret, trust, and audit algorithmic decisions. However, the selection of which aspects of AI systems to explain—features, models, outcomes, or decision paths—has significant ethical implications. Metrics for explainability often focus on technical comprehensibility or accuracy, while overlooking fairness, social context, and stakeholder relevance. This paper examines the ethical dimensions of explainability metrics, analyzing how choices in explanation design influence transparency, bias detection, accountability, and public trust. A conceptual framework is proposed for ethically aligned evaluation of XAI metrics, highlighting trade-offs between interpretability, privacy, and operational effectiveness. Understanding the ethical consequences of metric selection is essential to ensure AI systems serve societal and human-centered goals.

Keywords: Explainable AI, Ethics, Metrics, Transparency, Accountability, Bias