5 papers
MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups
Gideon Popoola, John Sheppard
Fairness in machine learning is predominantly evaluated through outcome-oriented metrics, such as Demographic parity, which measure whether predictions are statistically consistent…
GESD: Beyond Outcome-Oriented Fairness
Gideon Popoola, John Sheppard
Machine learning (ML) algorithms are increasingly deployed in high-stakes decision-making domains such as loan approvals, hiring, and recidivism predictions. While existing fairnes…
Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions
Gideon Popoola, John Sheppard
Machine learning algorithms in socially sensitive domains (e.g., credit decisions) often focus on equalizing predictive outcomes. However, satisfying these metrics does not guarant…
Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI
Gideon Popoola, John Sheppard
Machine learning algorithms are being used in high-stakes decisions, including those in criminal justice, healthcare, credit, and employment. The research community has responded w…
Procedural Fairness via Group Counterfactual Explanation
Gideon Popoola, John Sheppard
Fairness in machine learning research has largely focused on outcome-oriented fairness criteria such as Equalized Odds, while comparatively less attention has been given to procedu…