Learning to Ignore: Fair and Task Independent Representations
Training fair machine learning models, aiming for their interpretability and solving the problem of domain shift has gained a lot of interest in the last years. There is a vast amount of work addressing these topics, mostly in separation. In this work we show that they can be seen as a common framework of learning invariant representations. The representations should allow to predict the target while at the same time being invariant to sensitive attributes which split the dataset into subgroups. Our approach is based on the simple observation that it is impossible for any learning algorithm to differentiate samples if they have the same feature representation. This is formulated as an additional loss (regularizer) enforcing a common feature representation across subgroups. We apply it to learn fair models and interpret the influence of the sensitive attribute. Furthermore it can be used for domain adaptation, transferring knowledge and learning effectively from very few examples. In all applications it is essential not only to learn to predict the target, but also to learn what to ignore.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeDomain AdaptationSimilar Papers 제목 키워드 기반
MMD-B-Fair: Learning Fair Representations with Statistical Testing
We introduce a method, MMD-B-Fair, to learn fair representations of data via kernel two-sample testing. We find neural features of our data where a maximum mean discrepancy (MMD) test cannot distinguish between represent…
Representation LearningTwo-sample testingFrom Discrete to Continuous: Deep Fair Clustering With Transferable Representations
We consider the problem of deep fair clustering, which partitions data into clusters via the representations extracted by deep neural networks while hiding sensitive data attributes. To achieve fairness, existing methods…
ClusteringFairnessLearning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective
Performance unfairness among variables widely exists in multivariate time series (MTS) forecasting models since such models may attend/bias to certain (advantaged) variables. Addressing this unfairness problem is importa…
FairnessMultivariate Time Series ForecastingTime SeriesTime Series Analysis+1Fairness-aware Message Passing for Graph Neural Networks
Graph Neural Networks (GNNs) have shown great power in various domains. However, their predictions may inherit societal biases on sensitive attributes, limiting their adoption in real-world applications. Although many ef…
FairnessDe-biased Representation Learning for Fairness with Unreliable Labels
Removing bias while keeping all task-relevant information is challenging for fair representation learning methods since they would yield random or degenerate representations w.r.t. labels when the sensitive attributes co…
FairnessRepresentation Learning