Publications

One-network Adversarial Fairness

Tameem Adel, Isabel Valera, Zoubin Ghahramani, Adrian Weller, January 2019. (In 33rd AAAI Conference on Artificial Intelligence). Hawaii.

Abstract▼ URL

There is currently a great expansion of the impact of machine learning algorithms on our lives, prompting the need for objectives other than pure performance, including fairness. Fairness here means that the outcome of an automated decision-making system should not discriminate between subgroups characterized by sensitive attributes such as gender or race. Given any existing differentiable classifier, we make only slight adjustments to the architecture including adding a new hidden layer, in order to enable the concurrent adversarial optimization for fairness and accuracy. Our framework provides one way to quantify the tradeoff between fairness and accuracy, while also leading to strong empirical performance.

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

A.-H. Karimi, J. von Kügelgen, B. Schölkopf, I. Valera, 2020. (In Advances in Neural Information Processing Systems 33). Edited by H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, H. Lin. Curran Associates, Inc.. Note: *equal contribution.

Abstract▼ URL

Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration. Unfortunately, in practice, the true underlying structural causal model is generally unknown. In this work, we first show that it is impossible to guarantee recourse without access to the true structural equations. To address this limitation, we propose two probabilistic approaches to select optimal actions that achieve recourse with high probability given limited causal knowledge (e.g., only the causal graph). The first captures uncertainty over structural equations under additive Gaussian noise, and uses Bayesian model averaging to estimate the counterfactual distribution. The second removes any assumptions on the structural equations by instead computing the average effect of recourse actions on individuals similar to the person who seeks recourse, leading to a novel subpopulation-based interventional notion of recourse. We then derive a gradient-based procedure for selecting optimal recourse actions, and empirically show that the proposed approaches lead to more reliable recommendations under imperfect causal knowledge than non-probabilistic baselines.

Fair Decisions Despite Imperfect Predictions

Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera, 26–28 Aug 2020. (In 23rd International Conference on Artificial Intelligence and Statistics). Edited by Silvia Chiappa, Roberto Calandra. PMLR. Proceedings of Machine Learning Research.

Abstract▼ URL

Consequential decisions are increasingly informed by sophisticated data-driven predictive models. However, consistently learning accurate predictive models requires access to ground truth labels. Unfortunately, in practice, labels may only exist conditional on certain decisions—if a loan is denied, there is not even an option for the individual to pay back the loan. In this paper, we show that, in this selective labels setting, learning to predict is suboptimal in terms of both fairness and utility. To avoid this undesirable behavior, we propose to directly learn stochastic decision policies that maximize utility under fairness constraints. In the context of fair machine learning, our results suggest the need for a paradigm shift from “learning to predict” to “learning to decide”. Experiments on synthetic and real-world data illustrate the favorable properties of learning to decide, in terms of both utility and fairness.

On the Fairness of Causal Algorithmic Recourse

J. von Kügelgen, A.-H. Karimi, U. Bhatt, I. Valera, A. Weller, B. Schölkopf, 2022. (In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI)).

Abstract▼ URL

Algorithmic fairness is typically studied from the perspective of predictions. Instead, here we investigate fairness from the perspective of recourse actions suggested to individuals to remedy an unfavourable classification. We propose two new fairness criteria at the group and individual level, which – unlike prior work on equalising the average group-wise distance from the decision boundary – explicitly account for causal relationships between features, thereby capturing downstream effects of recourse actions performed in the physical world. We explore how our criteria relate to others, such as counterfactual fairness, and show that fairness of recourse is complementary to fairness of prediction. We study theoretically and empirically how to enforce fair causal recourse by altering the classifier and perform a case study on the Adult dataset. Finally, we discuss whether fairness violations in the data generating process revealed by our criteria may be better addressed by societal interventions as opposed to constraints on the classifier.

From parity to preference: Learning with cost-effective notions of fairness

M. B. Zafar, Isabel Valera, Manuel Rodriguez, Krishna P. Gummadi, Adrian Weller, December 2017. (In Advances in Neural Information Processing Systems 31). Long Beach, California.

Abstract▼ URL

The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups. In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems. However, the existing notions of fairness, based on parity (equality) in treatment or outcomes for different social groups, tend to be needlessly stringent, limiting the overall decision making accuracy. In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and propose preference-based notions of fairness —- given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the (dis)parity as compared to the other groups. Then, we introduce tractable proxies to design convex margin-based classifiers that satisfy these preference-based notions of fairness. Finally, we experiment with a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness.

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