FERMI: Fair Empirical Risk Minimization via Exponential R\'enyi Mutual Information

Despite the success of large-scale empirical risk minimization (ERM) at achieving high accuracy across a variety of machine learning tasks, fair ERM is hindered by the incompatibility of fairness constraints with stochastic optimization. In this paper, we propose the fair empirical risk minimization via exponential R\'enyi mutual information (FERMI) framework. FERMI is built on a stochastic estimator for exponential R\'enyi mutual information (ERMI), an information divergence measuring the degree of the dependence of predictions on sensitive attributes. Theoretically, we show that ERMI upper bounds existing popular fairness violation metrics, thus controlling ERMI provides guarantees on other commonly used violations, such as $L_\infty$. We derive an unbiased estimator for ERMI, which we use to derive the FERMI algorithm. We prove that FERMI converges for demographic parity, equalized odds, and equal opportunity notions of fairness in stochastic optimization. Empirically, we show that FERMI is amenable to large-scale problems with multiple (non-binary) sensitive attributes and non-binary targets. Extensive experiments show that FERMI achieves the most favorable tradeoffs between fairness violation and test accuracy across all tested setups compared with state-of-the-art baselines for demographic parity, equalized odds, equal opportunity. These benefits are especially significant for non-binary classification with large sensitive sets and small batch sizes, showcasing the effectiveness of the FERMI objective and the developed stochastic algorithm for solving it.Comment: 29 page

FairCanary: Rapid Continuous Explainable Fairness

Machine Learning (ML) models are being used in all facets of today's society to make high stake decisions like bail granting or credit lending, with very minimal regulations. Such systems are extremely vulnerable to both propagating and amplifying social biases, and have therefore been subject to growing research interest. One of the main issues with conventional fairness metrics is their narrow definitions which hide the complete extent of the bias by focusing primarily on positive and/or negative outcomes, whilst not paying attention to the overall distributional shape. Moreover, these metrics are often contradictory to each other, are severely restrained by the contextual and legal landscape of the problem, have technical constraints like poor support for continuous outputs, the requirement of class labels, and are not explainable. In this paper, we present Quantile Demographic Drift, which addresses the shortcomings mentioned above. This metric can also be used to measure intra-group privilege. It is easily interpretable via existing attribution techniques, and also extends naturally to individual fairness via the principle of like-for-like comparison. We make this new fairness score the basis of a new system that is designed to detect bias in production ML models without the need for labels. We call the system FairCanary because of its capability to detect bias in a live deployed model and narrow down the alert to the responsible set of features, like the proverbial canary in a coal mine

AI Fairness:from Principles to Practice

This paper summarizes and evaluates various approaches, methods, and techniques for pursuing fairness in artificial intelligence (AI) systems. It examines the merits and shortcomings of these measures and proposes practical guidelines for defining, measuring, and preventing bias in AI. In particular, it cautions against some of the simplistic, yet common, methods for evaluating bias in AI systems, and offers more sophisticated and effective alternatives. The paper also addresses widespread controversies and confusions in the field by providing a common language among different stakeholders of high-impact AI systems. It describes various trade-offs involving AI fairness, and provides practical recommendations for balancing them. It offers techniques for evaluating the costs and benefits of fairness targets, and defines the role of human judgment in setting these targets. This paper provides discussions and guidelines for AI practitioners, organization leaders, and policymakers, as well as various links to additional materials for a more technical audience. Numerous real-world examples are provided to clarify the concepts, challenges, and recommendations from a practical perspective

Information Theory and Machine Learning

The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems