论文标题
深入学习中的公平性
Towards Auditability for Fairness in Deep Learning
论文作者
论文摘要
群体公平指标可以检测到深度学习模型何时对有利和弱势群体的群体进行不同的行为，但是即使在这些指标上得分良好的模型也可以做出公然不公平的预测。我们提出了平稳的预测灵敏度，这是对深度学习模型的个人公平性的有效计算的量度，它受到深度学习中可解释性的想法的启发。平稳的预测灵敏度允许对个人预测进行公平审核。我们提出了初步的实验结果，表明平稳的预测灵敏度可以帮助区分公平和不公平的预测，并且可能有助于从“群体 - 费用”模型中检测出公然不公平的预测。
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.
