Machine Learning (ML) models are widely employed to drive many modern data systems. While they are undeniably powerful tools, ML models often demonstrate imbalanced performance and unfair behaviors. The root of this problem often lies in the fact that different subpopulations commonly display divergent trends: as a learning algorithm tries to identify trends in the data, it naturally favors the trends of the majority groups, leading to a model that performs poorly and unfairly for minority populations. Our goal is to improve the fairness and trustworthiness of ML models by applying only non-invasive interventions, i.e., without altering the data or the learning algorithm. We use a simple but key insight: the divergence of trends between different populations, and, consecutively, between a learned model and minority populations, is analogous to data drift, which indicates the poor conformance between parts of the data and the trained model. We explore two strategies (model-splitting and reweighing) to resolve this drift, aiming to improve the overall conformance of models to the underlying data. Both our methods introduce novel ways to employ the recently-proposed data profiling primitive of Conformance Constraints. Our experimental evaluation over 7 real-world datasets shows that both DifFair and ConFair improve the fairness of ML models. We demonstrate scenarios where DifFair has an edge, though ConFair has the greatest practical impact and outperforms other baselines. Moreover, as a model-agnostic technique, ConFair stays robust when used against different models than the ones on which the weights have been learned, which is not the case for other state of the art.

翻译：机器学习（ML）模型被广泛应用于驱动众多现代数据系统。尽管它们无疑是强大的工具，但ML模型经常表现出性能失衡和不公平行为。这一问题的根源通常在于不同子群体普遍呈现出相异的趋势：当学习算法试图识别数据中的趋势时，其自然倾向于多数群体的趋势，从而导致模型对少数群体表现不佳且不公平。我们的目标是通过仅施加非侵入式干预（即不改变数据或学习算法）来提升ML模型的公平性和可信度。我们利用了一个简单但关键的洞见：不同群体之间趋势的差异，以及随之而来的已学习模型与少数群体之间的差异，类似于数据漂移，这揭示了部分数据与训练模型之间的低契合度。我们探索了两种策略（模型拆分与重加权）来解决这种漂移，旨在提升模型与底层数据的整体契合度。两种方法都引入了新颖的方式，来利用近期提出的数据基础分析原语——一致性约束。在7个真实世界数据集上的实验评估表明，DifFair和ConFair均提升了ML模型的公平性。我们展示了DifFair具备优势的场景，尽管ConFair具有最大的实际影响并优于其他基线方法。此外，作为一种模型无关技术，ConFair在与权重学习时所用模型不同的模型上使用时仍保持稳健，而其他最先进的方法则不具备这一特性。