Data collected in the real world often encapsulates historical discrimination
against disadvantaged groups and individuals. Existing fair machine learning
(FairML) research has predominantly focused on mitigating discriminative bias
in the model prediction, with far less effort dedicated towards exploring how
to trace biases present in the data, despite its importance for the
transparency and interpretability of FairML. To fill this gap, we investigate a
novel research problem: discovering samples that reflect biases/prejudices from
the training data. Grounding on the existing fairness notions, we lay out a
sample bias criterion and propose practical algorithms for measuring and
countering sample bias. The derived bias score provides intuitive sample-level
attribution and explanation of historical bias in data. On this basis, we
further design two FairML strategies via sample-bias-informed minimal data
editing. They can mitigate both group and individual unfairness at the cost of
minimal or zero predictive utility loss. Extensive experiments and analyses on
multiple real-world datasets demonstrate the effectiveness of our methods in
explaining and mitigating unfairness. Code is available at
this https URL

数据收集中的历史性歧视对弱势群体和个体产生了影响。现有的公平机器学习研究主要致力于减轻模型预测中的歧视倾向，但对于如何追踪数据中存在的偏见却付出了较少的努力，尽管这对公平机器学习的透明度和可解释性非常重要。为了填补这一空白，我们研究了一个新的问题：发现在训练数据中反映偏见 / 偏见的样本。基于现有的公平性概念，我们提出了一个样本偏见准则，并提出了衡量和抵消样本偏见的实用算法。得出的偏见分数提供了历史偏见在数据中的直观样本级归因和说明。在此基础上，我们通过基于样本偏见的最小数据编辑设计了两种公平机器学习策略。它们可以在最小或零的预测效用损失的情况下，减轻群体和个体的不公平现象。对多个真实世界数据集的大量实验和分析证明了我们方法在解释和减轻不公平方面的有效性。代码可在此 URL 获取：https://URL

AIM：归因、解释、减轻数据不公平

AIM: Attributing, Interpreting, Mitigating Data Unfairness

Ensuring fairness in deep-learning-based segmentors is crucial for health
equity. Much effort has been dedicated to mitigating unfairness in the training
datasets or procedures. However, with the increasing prevalence of foundation
models in medical image analysis, it is hard to train fair models from scratch
while preserving utility. In this paper, we propose a novel method, Adversarial
Privacy-aware Perturbations on Latent Embedding (APPLE), that can improve the
fairness of deployed segmentors by introducing a small latent feature perturber
without updating the weights of the original model. By adding perturbation to
the latent vector, APPLE decorates the latent vector of segmentors such that no
fairness-related features can be passed to the decoder of the segmentors while
preserving the architecture and parameters of the segmentor. Experiments on two
segmentation datasets and five segmentors (three U-Net-like and two SAM-like)
illustrate the effectiveness of our proposed method compared to several
unfairness mitigation methods.

通过在潜在嵌入上引入微小的扰动，而不更新原始模型的权重，我们提出了一种新的方法 APPLE（Adversarial Privacy-aware Perturbations on Latent Embedding），以提高部署分割器的公平性，从而在保留分割器的架构和参数的同时，对分割器的潜在矢量进行装饰，以便不能传递与公平性相关的特征给分割器的解码器。与几种不公平性缓解方法相比，通过在两个分割数据集和五个分割器上进行的实验证明了我们提出的方法的有效性。

APPLE：隐嵌入的对抗性隐私感知扰动用于公平性缓解

APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for  Unfairness Mitigation

Data valuation is a ML field that studies the value of training instances
towards a given predictive task. Although data bias is one of the main sources
of downstream model unfairness, previous work in data valuation does not
consider how training instances may influence both performance and fairness of
ML models. Thus, we propose Fairness-Aware Data vauatiOn (FADO), a data
valuation framework that can be used to incorporate fairness concerns into a
series of ML-related tasks (e.g., data pre-processing, exploratory data
analysis, active learning). We propose an entropy-based data valuation metric
suited to address our two-pronged goal of maximizing both performance and
fairness, which is more computationally efficient than existing metrics. We
then show how FADO can be applied as the basis for unfairness mitigation
pre-processing techniques. Our methods achieve promising results -- up to a 40
p.p. improvement in fairness at a less than 1 p.p. loss in performance compared
to a baseline -- and promote fairness in a data-centric way, where a deeper
understanding of data quality takes center stage.

FADO 是一个数据评估框架，旨在将公平性考虑到与机器学习相关的任务（如数据预处理、探索性数据分析、主动学习）中，采用基于熵的数据估值指标来最大化性能和公平性，可作为不公平性缓解预处理技术的基础，对于质量较好的数据具有重要意义。