Research map / Learning theory and algorithms / Fairness
Fairness with Censorship: Bridging the Gap between Fairness Research and Real-World Deployment
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Revisits AI fairness under censored class labels, motivating fairness research for real-world settings such as precision medicine, actuarial analysis and recidivism prediction.
Problem
Most fairness methods assume the class label is available, which is impractical in applications like precision medicine, actuarial analysis and recidivism prediction where labels are censored.
Approach
Revisits fairness under label censorship and reveals idiosyncrasies of fairness when labels are censored, aiming to bridge fairness research and real-world deployment.
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