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）模型广泛应用于驱动现代数据系统。尽管是不可否认的强大工具，ML模型常表现出性能失衡与不公平行为。这一问题的根源通常在于不同子群体普遍呈现差异化趋势：当学习算法试图识别数据中的趋势时，会自然偏向多数群体的趋势，导致模型对少数群体的表现不佳且不公平。本研究目标是通过仅应用非侵入式干预（即不改变数据或学习算法）来提升ML模型的公平性与可信度。我们利用一个简单但关键的洞察：不同群体间趋势的差异，以及由此衍生的学习模型与少数群体之间的差异，类似于数据漂移现象——数据各部分与训练模型之间的低一致性。我们探索两种策略（模型分割与权重重分配）来解决这种漂移，旨在提升模型对底层数据的整体一致性。两种方法均引入了对最近提出的数据画像原语——一致性约束（Conformance Constraints）的创新应用。基于7个真实数据集的实验评估表明，DifFair和ConFair均能提升ML模型的公平性。我们展示了DifFair具有优势的场景，但ConFair具有最大实际影响力且优于其他基线方法。此外，作为模型无关技术，ConFair在应用于非权重学习模型时仍保持鲁棒性，而现有最优方法无法实现这一点。