发现公平性与准确率在概率模型中存在正相关，可能打破二者权衡困境。
On the Correlation between Individual Fairness and Predictive Accuracy in Probabilistic Models
- 通过后验推断对隐私特征扰动的鲁棒性评估个体公平性
- 14个含公平性问题的数据集实验验证鲁棒性强的样本更易分类正确
- 提出新方法降低贝叶斯网络中多隐私特征分析的计算复杂度
我们通过分析后验推断对隐私特征扰动的鲁棒性，研究生成式概率分类器中的个体公平性。基于鲁棒性分析的已有成果，我们假设鲁棒性与预测准确率存在相关性：鲁棒性越强的实例越可能被准确分类。在包含14个具有公平性关切的数据集的基准上，采用贝叶斯网络作为生成模型，对这一假设进行实证检验。为解决贝叶斯网络在多个隐私特征上进行鲁棒性分析时的计算复杂性，我们将问题重构为辅助马尔可夫随机场中的最可能解释任务。实验结果支持该假设，表明在个体公平性与预测准确率之间存在潜在正相关，为缓解传统公平性-准确率权衡提供了新方向。
原文摘要 · Abstract (English)
We investigate individual fairness in generative probabilistic classifiers by analysing the robustness of posterior inferences to perturbations in private features. Building on established results in robustness analysis, we hypothesise a correlation between robustness and predictive accuracy, specifically, instances exhibiting greater robustness are more likely to be classified accurately. We empirically assess this hypothesis using a benchmark of fourteen datasets with fairness concerns, employing Bayesian networks as the underlying generative models. To address the computational complexity associated with robustness analysis over multiple private features with Bayesian networks, we reformulate the problem as a most probable explanation task in an auxiliary Markov random field. Our experiments confirm the hypothesis about the correlation, suggesting novel directions to mitigate the traditional trade-off between fairness and accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。