Towards Auditability for Fairness in Deep Learning

• URL: http://arxiv.org/abs/2012.00106v1
• Date: Mon, 30 Nov 2020 21:28:12 GMT
• Title: Towards Auditability for Fairness in Deep Learning
• Authors: Ivoline C. Ngong, Krystal Maughan, Joseph P. Near
• Abstract summary: Group fairness metrics can detect when a deep learning model behaves differently for advantaged and disadvantaged groups. We present smooth prediction sensitivity, an efficiently computed measure of individual fairness for deep learning models.
• Score: 1.052782170493037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Group fairness metrics can detect when a deep learning model behaves differently for advantaged and disadvantaged groups, but even models that score well on these metrics can make blatantly unfair predictions. We present smooth prediction sensitivity, an efficiently computed measure of individual fairness for deep learning models that is inspired by ideas from interpretability in deep learning. smooth prediction sensitivity allows individual predictions to be audited for fairness. We present preliminary experimental results suggesting that smooth prediction sensitivity can help distinguish between fair and unfair predictions, and that it may be helpful in detecting blatantly unfair predictions from "group-fair" models.

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