  The widespread adoption of ML systems across critical domains like hiring,
finance, and healthcare raises growing concerns about their potential for
discriminatory decision-making based on protected attributes. While efforts to
ensure fairness during development are crucial, they leave deployed ML systems
vulnerable to potentially exhibiting discrimination during their operations. To
address this gap, we propose a novel framework for on-the-fly tracking and
correction of discrimination in deployed ML systems. Leveraging counterfactual
explanations, the framework continuously monitors the predictions made by an ML
system and flags discriminatory outcomes. When flagged, post-hoc explanations
related to the original prediction and the counterfactual alternatives are
presented to a human reviewer for real-time intervention. This
human-in-the-loop approach empowers reviewers to accept or override the ML
system decision, enabling fair and responsible ML operation under dynamic
settings. While further work is needed for validation and refinement, this
framework offers a promising avenue for mitigating discrimination and building
trust in ML systems deployed in a wide range of domains.
