An End-to-End Context Aware Anomaly Detection System
Bhanukiran Vinzamuri, Elham Khabiri, et al.
Big Data 2020
Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling group discrimination, limiting distortion in individual data samples, and preserving utility. Several theoretical properties are established, including conditions for convexity, a characterization of the impact of limited sample size on discrimination and utility guarantees, and a connection between discrimination and estimation. Two instances of the proposed optimization are applied to datasets, including one on real-world criminal recidivism. Results show that discrimination can be greatly reduced at a small cost in classification accuracy and with precise control of individual distortion.
Bhanukiran Vinzamuri, Elham Khabiri, et al.
Big Data 2020
Dennis Wei, Kush R. Varshney
ICASSP 2015
Kartik Ahuja, Prasanna Sattigeri, et al.
UAI 2021
Flavio Du Pin Calmon, Dennis Wei, et al.
NeurIPS 2017