Measuring and Mitigating Bias for Tabular Datasets with Multiple Protected Attributes
Motivated by the recital (67) of the current corrigendum of the AI Act in the European Union, we propose and present measures and mitigation strategies for discrimination in tabular datasets. We specifically focus on datasets that contain multiple protected attributes, such as nationality, age, and sex. This makes measuring and mitigating bias more challenging, as many existing methods are designed for a single protected attribute. This paper comes with a twofold contribution: Firstly, new discrimination measures are introduced. These measures are categorized in our framework along with existing ones, guiding researchers and practitioners in choosing the right measure to assess the fairness of the underlying dataset. Secondly, a novel application of an existing bias mitigation method, FairDo, is presented. We show that this strategy can mitigate any type of discrimination, including intersectional discrimination, by transforming the dataset. By conducting experiments on real-world datasets (Adult, Bank, COMPAS), we demonstrate that de-biasing datasets with multiple protected attributes is possible. All transformed datasets show a reduction in discrimination, on average by 28%. Further, these datasets do not compromise any of the tested machine learning models' performances significantly compared to the original datasets. Conclusively, this study demonstrates the effectiveness of the mitigation strategy used and contributes to the ongoing discussion on the implementation of the European Union's AI Act.
Code (1)
Tasks
AttributeFairnessMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
ProbLog4Fairness: A Neurosymbolic Approach to Modeling and Mitigating Bias
Operationalizing definitions of fairness is difficult in practice, as multiple definitions can be incompatible while each being arguably desirable. Instead, it may be easier to directly describe algorithmic bias through …
Measuring Social Biases in Grounded Vision and Language Embeddings
We generalize the notion of social biases from language embeddings to grounded vision and language embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungroun…
Word EmbeddingsAIM: Attributing, Interpreting, Mitigating Data Unfairness
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 discrim…
FairnessConQUR: Mitigating Delusional Bias in Deep Q-learning
Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we deve…
Atari GamesQ-LearningLarge Language Models Engineer Too Many Simple Features For Tabular Data
Tabular machine learning problems often require time-consuming and labor-intensive feature engineering. Recent efforts have focused on using large language models (LLMs) to capitalize on their potential domain knowledge.…
Feature EngineeringText Generation