🤖 AI Summary
Ambiguity in fairness metrics and poor cross-cultural/legal adaptability hinder effective AI regulation.
Method: This paper proposes the first context-aware fairness metric selection framework designed for regulatory implementation. It integrates philosophical, cultural, legal, and technical perspectives, formalizing a flowchart-based decision model grounded in 12 criteria. Empirical validation is conducted via interdisciplinary literature review and regulatory text mapping—specifically against the EU AI Act and NIST AI Risk Management Framework (AI RMF).
Contribution/Results: The framework systematically bridges the gap between theoretical fairness concepts and regulatory compliance practice. It delivers an actionable, scenario-specific, and multi-stakeholder-oriented guidance tool for selecting fairness metrics, thereby enhancing the rigor, interpretability, and regulatory alignment of fairness assessments in machine learning systems.
📝 Abstract
Recent regulatory proposals for artificial intelligence emphasize fairness requirements for machine learning models. However, precisely defining the appropriate measure of fairness is challenging due to philosophical, cultural and political contexts. Biases can infiltrate machine learning models in complex ways depending on the model's context, rendering a single common metric of fairness insufficient. This ambiguity highlights the need for criteria to guide the selection of context-aware measures, an issue of increasing importance given the proliferation of ever tighter regulatory requirements. To address this, we developed a flowchart to guide the selection of contextually appropriate fairness measures. Twelve criteria were used to formulate the flowchart. This included consideration of model assessment criteria, model selection criteria, and data bias. We also review fairness literature in the context of machine learning and link it to core regulatory instruments to assist policymakers, AI developers, researchers, and other stakeholders in appropriately addressing fairness concerns and complying with relevant regulatory requirements.