"Fairness fatigue" is a pressing concern in AI governance, where repeated attempts to address bias in AI models lead to diminishing returns and frustration among stakeholders. This phenomenon underscores the need for AI developers to strike a delicate balance between competing priorities: fairness, efficiency, and scalability.
As AI models become increasingly sophisticated, the pursuit of fairness can sometimes conflict with other objectives. For instance, tweaking an AI model to reduce bias might lead to a decrease in its overall accuracy or a trade-off in processing speed. This paradox highlights the importance of establishing clear goals and metrics for fairness, which can vary depending on the context and application.
A classic example is the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm, used to predict recidivism rates. Initially designed to reduce racial bias in sentencing, the model was found to perpetuate existing disparities. The...
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