通过移除由于受保护的属性而导致的不需要的变化来消除表示的偏见
Debiasing representations by removing unwanted variation due to protected attributes
摘要：
我们提出了一种基于回归的方法来消除隐含的偏见表示。在观察到受保护属性的任务上，该方法在统计上比已知方法更有效。此外，我们表明，这种方法导致去偏表示，满足一阶近似的条件奇偶校验。最后，我们证明了所提出的方法的有效性，减少种族偏见的累犯风险分数。
英文摘要：
We propose a regression-based approach to removing implicit biases in representations. On tasks where the protected attribute is observed, the method is statistically more efficient than known approaches. Further, we show that this approach leads to debiased representations that satisfy a first order approximation of conditional parity. Finally, we demonstrate the efficacy of the proposed approach by reducing racial bias in recidivism risk scores.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
