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Abstract: Black-box risk scoring models permeate our lives, yet are typically proprietary and opaque. We propose a transparent model distillation approach to understand and detect bias in such models. Model distillation was originally designed to distill knowledge from a large, complex model (the teacher model) to a faster, simpler model (the student model) without significant loss in prediction accuracy.
[1710.06169] Detecting Bias in Black-Box Models Using Transparent Model Distillation 
Added 9 months ago by Francis Tseng
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[1710.06169] Detecting Bias in Black-Box Models Using Transparent Model Distillation 
Info
Abstract: Black-box risk scoring models permeate our lives, yet are typically proprietary and opaque. We propose a transparent model distillation approach to understand and detect bias in such models. Model distillation was originally designed to distill knowledge from a large, complex model (the teacher model) to a faster, simpler model (the student model) without significant loss in prediction accuracy.
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