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On the Fairness of Causal Algorithmic Recourse
Julius von Kügelgen · Amir-Hossein Karimi · Umang Bhatt · Isabel Valera · Adrian Weller · Bernhard Schölkopf · Amir-Hossein Karimi

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in ICML Workshop on Algorithmic Recourse »

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.

Author Information

Julius von Kügelgen (MPI for Intelligent Systems, Tübingen & University of Cambridge)
Amir-Hossein Karimi (MPI for Intelligent Systems, Tübingen, Germany)
Umang Bhatt (University of Cambridge)
Isabel Valera (Uni Saarland)

Isabel Valera is a full Professor on Machine Learning at the Department of Computer Science of Saarland University in Saarbrücken (Germany), and Adjunct Faculty at MPI for Software Systems in Saarbrücken (Germany). She is also a scholar of the European Laboratory for Learning and Intelligent Systems (ELLIS). Prior to this, she was an independent group leader at the MPI for Intelligent Systems in Tübingen (Germany). She has held a German Humboldt Post-Doctoral Fellowship, and a “Minerva fast track” fellowship from the Max Planck Society. She obtained her PhD in 2014 and MSc degree in 2012 from the University Carlos III in Madrid (Spain), and worked as postdoctoral researcher at the MPI for Software Systems (Germany) and at the University of Cambridge (UK). Her research focuses on developing machine learning methods that are flexible, robust, interpretable and fair to analyze real-world data.

Adrian Weller (University of Cambridge, Alan Turing Institute)
Adrian Weller

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, and is a Turing AI Fellow leading work on trustworthy Machine Learning (ML). He is a Principal Research Fellow in ML at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.

Bernhard Schölkopf (MPI for Intelligent Systems Tübingen, Germany)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

Amir-Hossein Karimi (Max Planck Institute for Intelligent Systems)

Amir-Hossein Karimi is a final-year PhD candidate at ETH Zurich and the Max Planck Institute for Intelligent Systems, working under the guidance of Prof. Dr. Bernhard Schölkopf and Prof. Dr. Isabel Valera. His research interests lie at the intersection of causal inference, explainable AI, and program synthesis. Amir's contributions to the problem of algorithmic recourse have been recognized through spotlight and oral presentations at top venues such as NeurIPS, ICML, AAAI, AISTATS, ACM-FAccT, and ACM-AIES. He has also authored a book chapter and a highly-regarded survey paper in the ACM Computing Surveys. Supported by the NSERC, CLS, and Google PhD fellowships, Amir's research agenda aims to address the need for systems that make use of the best of both human and machine capabilities towards building trustworthy systems for human-machine collaboration. Prior to his PhD, Amir earned several awards including the Spirit of Engineering Science Award (UofToronto, 2015) and the Alumni Gold Medal Award (UWaterloo, 2018) for notable community and academic performance. Alongside his education, Amir gained valuable industry experience at Facebook, Google Brain, and DeepMind, and has provided >$250,000 in AI-consulting services to various startups and incubators. Finally, Amir teaches introductory and advanced topics in AI to an online community @PrinceOfAI.

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