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1. Abstracting Fairness: Oracles, Metrics, and Interpretability

   Dwork, C; Ilvento, C; Rothblum, G; Sur, P (January 2020, Proceedings of the 1st Symposium on Foundations of Responsible Computing (FORC 2020))

   Roth, A (Ed.)

   It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account. We examine what can be learned from a fairness oracle equipped with an underlying understanding of “true” fairness. The oracle takes as input a (context, classifier) pair satisfying an arbitrary fairness definition, and accepts or rejects the pair according to whether the classifier satisfies the underlying fairness truth. Our principal conceptual result is an extraction procedure that learns the underlying truth; moreover, the procedure can learn an approximation to this truth given access to a weak form of the oracle. Since every “truly fair” classifier induces a coarse metric, in which those receiving the same decision are at distance zero from one another and those receiving different decisions are at distance one, this extraction process provides the basis for ensuring a rough form of metric fairness, also known as individual fairness. Our principal technical result is a higher fidelity extractor under a mild technical constraint on the weak oracle’s conception of fairness. Our framework permits the scenario in which many classifiers, with differing outcomes, may all be considered fair. Our results have implications for interpretablity – a highly desired but poorly defined property of classification systems that endeavors to permit a human arbiter to reject classifiers deemed to be“unfair” or illegitimately derived. 

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2. Quantifying Individual Fairness in Continual Federated Learning

   https://doi.org/10.1145/3701716.3717646  

   Noor, Naima; Mashhadi, Afra (May 2025, ACM)

   Continual Federated Learning (CFL) is a distributed machine learning technique that enables multiple clients to collaboratively train a shared model without sharing their data, while also adapting to new classes without forgetting previously learned ones. This dynamic, adaptive learning process parallels the concept of founda- tion models in FL, where large, pre-trained models are fine-tuned in a decentralized, federated setting. While foundation models in FL leverage pre-trained knowledge as a starting point, CFL continu- ously updates the shared model as new tasks and data distributions emerge, requiring ongoing adaptation. Currently, there are limited evaluation models and metrics in measuring fairness in CFL, and ensuring fairness over time can be challenging as the system evolves. To address this challenge, this article explores temporal fairness in CFL, examining how the fairness of the model can be influenced by the selection and participation of clients over time. Based on individual fairness, we introduce a novel fairness metric that captures temporal aspects of client behavior and evaluates different client selection strategies for their impact on promoting fairness. 

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3. A Framework for Measuring and Benchmarking Fairness of Generative Crowd-Flow Models

   https://doi.org/10.1145/3724409  

   He, Jiaqi; Sheera, Apoorva; McFarland, Jack; Wang, Feiyang; Khullar, Manurag; Chavan, Sakshi; Herman, Bernease; Ugurel, Ekin; Mashhadi, Afra (April 2025, ACM Journal on Computing and Sustainable Societies)

   Urban population growth has significantly complicated the management of mobility systems, demanding innovative tools for planning. Generative Crowd-Flow  (GCF) models, which leverage machine learning to simulate urban movement patterns, offer a promising solution but lack sufficient evaluation of their fairness–a critical factor for equitable urban planning. We present an approach to measure and benchmark the fairness of GCF  models by developing a first-of-its-kind set of fairness metrics specifically tailored for this purpose. Using observed flow data, we employ a stochastic biased sampling approach to generate multiple permutations of Origin-Destination  datasets, each demonstrating intentional bias. Our proposed framework allows for the comparison of multiple GCF  models to evaluate how models introduce bias in outputs. Preliminary results indicate a tradeoff between model accuracy and fairness, underscoring the need for careful consideration in the deployment of these technologies. To this end, this study bridges the gap between human mobility literature and fairness in machine learning, with potential to help urban planners and policymakers leverage GCF  models for more equitable urban infrastructure development. 

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4. Censored Fairness through Awareness

   https://doi.org/10.1609/aaai.v37i12.26708  

   Zhang, Wenbin; Hernandez-Boussard, Tina; Weiss, Jeremy (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   There has been increasing concern within the machine learning community and beyond that Artificial Intelligence (AI) faces a bias and discrimination crisis which needs AI fairness with urgency. As many have begun to work on this problem, most existing work depends on the availability of class label for the given fairness definition and algorithm which may not align with real-world usage. In this work, we study an AI fairness problem that stems from the gap between the design of a fair model in the lab and its deployment in the real-world. Specifically, we consider defining and mitigating individual unfairness amidst censorship, where the availability of class label is not always guaranteed due to censorship, which is broadly applicable in a diversity of real-world socially sensitive applications. We show that our method is able to quantify and mitigate individual unfairness in the presence of censorship across three benchmark tasks, which provides the first known results on individual fairness guarantee in analysis of censored data. 

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5. Opportunistic Multi-aspect Fairness through Personalized Re-ranking

   https://doi.org/10.1145/3340631.3394846  

   Sonboli, Nasim; Eskandanian, Farzad; Burke, Robin; Liu, Weiwen; Mobasher, Bamshad (July 2020, ACM Conference on User Modeling, Adaptation and Personalization (UMAP))

   As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with recommendation accuracy. However, the previous work had largely ignored how individual preferences may limit the ability of an algorithm to produce fair recommendations. Furthermore, with few exceptions, researchers have only considered scenarios in which fairness is measured relative to a single sensitive feature or attribute (such as race or gender). In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results. Specifically, we show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions. 

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