FAIRER：将公平性视为决策依据的对齐
FAIRER: Fairness as Decision Rationale Alignment
AI总结：
本文从决策依据对齐角度研究DNN公平性，提出参数对齐分数和梯度引导的对齐方法，通过约束中间和最终神经元响应一致性，显著提升公平性并保持高准确率。
摘要：
深度神经网络（DNNs）取得了显著进展，但经常面临公平性问题，因为深度模型通常在某些子群体（例如男性和女性）之间表现出明显的准确率差异。现有研究通过采用感知公平性的损失函数来约束最后一层输出并直接正则化DNNs，以解决这一关键问题。尽管DNNs的公平性得到了改善，但目前尚不清楚训练好的网络是如何做出公平预测的，这限制了未来公平性的提升。在本文中，我们从决策依据的角度研究公平性，并定义了参数对齐分数，通过分析各种子群体中的神经元影响来表征网络的公平决策过程。大量实证研究表明，不公平问题可能源于子群体之间未对齐的决策依据。现有的公平性正则化项未能实现决策依据对齐，因为它们仅约束最后一层输出，而忽略了中间神经元对齐。为了解决这个问题，我们将公平性定义为一项新任务，即决策依据对齐，要求DNNs的神经元在中间过程和最终预测中都对子群体具有一致的响应。为了在优化过程中使这一想法切实可行，我们放宽了朴素目标函数，并提出了梯度引导的对齐对齐，鼓励跨子群体神经元的梯度加权一致性。在各种数据集上的大量实验表明，我们的方法能够显著增强公平性，同时保持高水平的准确率，并以大幅优势胜过其他方法。
英文摘要：
Deep neural networks (DNNs) have made significant progress, but often suffer from fairness issues, as deep models typically show distinct accuracy differences among certain subgroups (e.g., males and females). Existing research addresses this critical issue by employing fairness-aware loss functions to constrain the last-layer outputs and directly regularize DNNs. Although the fairness of DNNs is improved, it is unclear how the trained network makes a fair prediction, which limits future fairness improvements. In this paper, we investigate fairness from the perspective of decision rationale and define the parameter parity score to characterize the fair decision process of networks by analyzing neuron influence in various subgroups. Extensive empirical studies show that the unfair issue could arise from the unaligned decision rationales of subgroups. Existing fairness regularization terms fail to achieve decision rationale alignment because they only constrain last-layer outputs while ignoring intermediate neuron alignment. To address the issue, we formulate the fairness as a new task, i.e., decision rationale alignment that requires DNNs' neurons to have consistent responses on subgroups at both intermediate processes and the final prediction. To make this idea practical during optimization, we relax the naive objective function and propose gradient-guided parity alignment, which encourages gradient-weighted consistency of neurons across subgroups. Extensive experiments on a variety of datasets show that our method can significantly enhance fairness while sustaining a high level of accuracy and outperforming other approaches by a wide margin.
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