A hybrid NeurIPS 2022 Workshop
Algorithmic Fairness through the Lens of Causality and Privacy
The Algorithmic Fairness through the Lens of Causality and Privacy (AFCP) workshop aims to spark discussions on how open questions in algorithmic fairness can be addressed with causality and privacy.
As machine learning models permeate every aspect of decision making systems in consequential areas such as healthcare and criminal justice, it has become critical for these models to satisfy trustworthiness desiderata such as fairness, interpretability, accountability, privacy and security. Initially studied in isolation, recent work has emerged at the intersection of these different fields of research, leading to interesting questions on how fairness can be achieved using a causal perspective and under privacy concerns.
Indeed, the field of causal fairness has seen a large expansion in recent years notably as a way to counteract the limitations of initial statistical definitions of fairness. While a causal framing provides flexibility in modelling and mitigating sources of bias using a causal model, proposed approaches rely heavily on assumptions about the data generating process, i.e., the faithfulness and ignorability assumptions. This leads to open discussions on (1) how to fully characterize causal definitions of fairness, (2) how, if possible, to improve the applicability of such definitions, and (3) what constitutes a suitable causal framing of bias from a sociotechnical perspective?
Additionally, while most existing work on causal fairness assumes observed sensitive attribute data, such information is likely to be unavailable due to, for example, data privacy laws or ethical considerations. This observation has motivated initial work on training and evaluating fair algorithms without access to sensitive information and studying the compatibility and trade-offs between fairness and privacy. However, such work has been limited, for the most part, to statistical definitions of fairness raising the question of whether these methods can be extended to causal definitions.
Given the interesting questions that emerge at the intersection of these different fields, this workshop aims to deeply investigate how these different topics relate, but also how they can augment each other to provide better or more suited definitions and mitigation strategies for algorithmic fairness.
Causality & Fairness
Dhanya Sridhar (U. of Montreal, Mila)
Niki Kilbertus (TUM, Helmholtz AI)
David Madras (U. of Toronto)
Amanda Coston (CMU)
Privacy & Fairness
Claire Bowen (Urban Institute)
Ulrich Aïvodji (ETS Montreal)
Fatemehsadat Mireshghallah (UCSD)
Interpretability & Fairness
Zachary Lipton (CMU)
Julius Adebayo (MIT)
Amir-Hossein Karimi (MPI-IS, ETH Zurich)
Ethics & Fairness
Negar Rostamzadeh (Google Research)
Sina Fazelpour (Northeastern U.)
Nyalleng Moroosi (Google Research)
4-8 pages (not including references and appendix), NeurIPS format
Submission portal (tba)
Submissions to the Paper track should describe new projects aimed at using Causality and/or Privacy to address fairness in machine learning. Submissions should have theoretical or empirical results demonstrating the approach, and specifying how the project fills a gap in the current literature. Authors of accepted papers will be required to upload a 10-min video presentation of their paper. All recorded talks will be made available on the workshop website.
We welcome submissions of novel work in the area of fairness with a special interest on (but not limited to):
- Failure modes of current fairness definitions
- Fairness metrics and mitigation techniques
- Novel, application-specific formalizations of fairness
- Causal definitions of fairness
- Applications of counterfactual fairness
- Methods to encode domain-specific fairness knowledge into causal models
- Studies of practical limitations of causally grounded fairness methods
- Role of counterfactuals in studies of discrimination
- Compatibility of fairness and privacy notions
- Learning fair algorithms from encrypted data
- Privacy-preserving learning of fair algorithms
- Privacy-preserving auditing of algorithmic fairness
- Trade-offs between privacy, fairness and utility
- Policy aspects of data privacy and fairness
Sep 15 Sep 22, 2022 AoE
Sep 22 Sep 26, 2022 AoE
Format: 4-8 pages not including references and appendix. The impact statement or checklist are optional and do not count towards the page limit.
1 page (max, anonymized) in pdf format
Submission portal (tba)