Two of the trickiest qualities to balance in the world of machine learning are fairness and accuracy. Algorithms optimized for accuracy may unintentionally perpetuate bias against specific groups, while those prioritizing fairness may compromise accuracy by misclassifying some data points.
With this challenge in mind, a team from CSAIL has taken the lead in devising a framework that enables a more nuanced approach to balancing these qualities.
Instead of forcing a binary decision in labeling all data points as “good” or “bad,” their framework uses their Reject Option Classification (ROC) algorithm which assigns a third category of “rejected samples,” allowing it to identify instances where the model might be less certain or where predictions could potentially le...
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