AdaFair: Cumulative Fairness Adaptive Boosting

By Vasileios Iosifidis, Eirini Ntoutsi. 2019

97 citations recorded by the source corpus.

The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot of concerns regarding potential discrimination. In particular, in certain cases it has been observed that ML algorithms can provide different decisions based on sensitive attributes such as gender or race and therefore can lead to discrimination. Although, several fairness-aware ML approaches have been proposed, their focus has been largely on preserving the overall classification accuracy while improving fairness in predictions for both protected and non-protected groups (defined based on the sensitive attribute(s)). The overall accuracy however is not a good indicator of performance in case of class imbalance, as it is biased towards the majority class. As we will see in our experiments, many of the fairness-related datasets suffer from class imbalance and therefore, tackling fairness requires also tackling the imbalance problem. To this end, we propose AdaFair, a fairness-aware classifier based on AdaBoost that further updates the weights of the instances in each boosting round taking into account a cumulative notion of fairness based upon all current ensemble members, while explicitly tackling class-imbalance by optimizing the number of ensemble members for balanced classification error. Our experiments show that our approach can achieve parity in true positive and true negative rates for both protected and non-protected groups, while it significantly outperforms existing fairness-aware methods up to 25% in terms of balanced error.

Subfields: Artificial Intelligence, Safety Research

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Citations in the corpus

Mathematical optimization in classification and regression trees (2021, background): “The use of Machine Learning models in socially sensitive decision-making calls for analyzing their fairness (Iosifidis and Ntoutsi 2019; Miron et al. 2020; Zafar et al. 2017).”
FairBatch: Batch Selection for Model Fairness (2020, methodology): “, 2018], or boosting [Iosifidis and Ntoutsi, 2019]; see more related works discussed in depth in Sec.”
Fairness in Machine Learning: A Survey (2020, background): “In gradient descent-based models, several in-processing approaches have been suggested to learn instance weights such that fairness can be improved [149, 207, 227, 246].”
Bias in data‐driven artificial intelligence systems—An introductory survey (2020, background): “Iosifidis and Ntoutsi (2019) propose a sequential fair ensemble, AdaFair, that extends the weighted distribution approach of AdaBoost by also considering the cumulative fairness of the learner up to the current boosting round and moreover, it optimizes for balanced error instead of overall error to…”