Optimising Equal Opportunity Fairness in Model Training (2022)

Abstract: Real-world datasets often encode stereotypes and societal biases. Such biases can be implicitly captured by trained models, leading to biased predictions and exacerbating existing societal preconceptions. Existing debiasing methods, such as adversarial training and removing protected information from representations, have been shown to reduce bias. However, a disconnect between fairness criteria and training objectives makes it difficult to reason theoretically about the effectiveness of different techniques. In this work, we propose two novel training objectives which directly optimise for the widely-used criterion of equal opportunity , and show that they are effective in reducing bias while maintaining high performance over two classification tasks.

## introduction and background modern neural machine learning has achieved great success across a range of classification tasks. however, when applied over real-world data, especially in high-stakes settings such as hiring processes and loan approvals, care must be taken to assess the fairness of models. this is because real-world datasets generally encode societal preconceptions and stereotypes, thereby leading to models trained on such datasets to amplify existing bias and make biased predictions (i.e., models perform unequally towards different subgroups of individuals). this kind of unfairness has been reported over various nlp tasks, such as part-of-speech tagging @xcite @xcite , sentiment analysis @xcite , and image activity recognition @xcite . various methods have been proposed to mitigate bias, including adversarial training, and preand post-processing strategies. adversarial training aims to make it difficult for a discriminator to * equal contributors to this work. however, training strategies and optimisation objectives are generally disconnected from fairness metrics which directly measure the extent to which different groups are treated (in)equitably. this makes it difficult to understand the effectiveness of previous debiasing methods from a theoretical perspective. in this work, we propose to explicitly incorporate equal opportunity into our training objective, thereby achieving bias reduction. this paper makes the following contributions: our code is available at: https://github.com/ ailiaili/difference_mean_fair_models . ## experiments our experiments compare the performance and fairness of our methods against various competitive baselines, and across two classification tasks. ## analysis to better understand the effectiveness of our proposed methods, we perform two sets of experiments: (1) an ablation study, and (2) an analysis of training efficiency. ## conclusion we proposed to incorporate fairness criteria into model training, in explicitly optimising for equal opportunity by minimising the loss difference over different subgroups conditioned on the target label. to deal with data imbalance based on the targetlabel, we proposed a variant of our method which promotes fairness across all target labels. experimental results over twitter sentiment analysis and profession classification tasks show the effectiveness and flexibility of our proposed methods.