TY - JOUR
T1 - Interactive Explainable Fair Machine Learning: A Human-in-the-Loop Framework for Bias Detection and Mitigation
AU - James Walker
AU - Olivia Harris
AU - George Wilson
JF - Journal of Trustworthy and Explainable Intelligent Systems
JO - J Trustworthy Explain Intell Syst
Y1 - 2025
VL - 4
IS - 2
SP - 68
N2 - The expansion of machine-learning frameworks into sectors with significant end-user consequences has heightened concerns regarding the equity of trained models, particularly the necessity of preventing biases that disadvantage distinct demographic sub-groups. The majority of current bias-mitigation techniques adjust the relative weight of data observations during the pre-processing phase. Because fairness depends heavily on context, we support an interactive machine-learning strategy that empowers human supervisors to offer iterative guidance for model refinement. To this end, we adapt the explanatory interactive machine-learning framework Caipi for equitable machine learning. This modified approach, FairCaipi, integrates human-in-the-loop interventions regarding both model predictions and underlying explanations to enhance model equity. Empirical outcomes indicate that FairCaipi outpaces a state-of-the-art pre-processing bias mitigation strategy in both the equity metrics and the predictive accuracy of the resulting machine-learning framework. We show that FairCaipi serves to expose and minimize algorithmic bias in machine-learning architectures while simultaneously identifying underlying human bias.
UR - https://tipublications.com/t334432937
ER -