# Fairness Through Computationally-Bounded Awareness

arXiv: https://arxiv.org/abs/1803.03239 · HTML: https://mlsanity.com/paper/1803.03239/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 30 of 55)

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- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [Practical Adversarial Multivalid Conformal Prediction](https://mlsanity.com/paper/2206.01067/) (2022)
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- [Low-Degree Multicalibration](https://mlsanity.com/paper/2203.01255/) (2022)
- [A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges](https://mlsanity.com/paper/2305.06969/) (2023)
- [Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities](https://mlsanity.com/paper/2102.04257/) (2021)
- [Making Decisions under Outcome Performativity](https://mlsanity.com/paper/2210.01745/) (2022)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Algorithm Fairness in AI for Medicine and Healthcare](https://mlsanity.com/paper/2110.00603/) (2021)
- [A Survey on Fairness for Machine Learning on Graphs](https://mlsanity.com/paper/2205.05396/) (2022)
- [What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective](https://mlsanity.com/paper/2206.04101/) (2022)
- [Modeling Techniques for Machine Learning Fairness: A Survey](https://mlsanity.com/paper/2111.03015/) (2021)
- [Omnipredictors for Constrained Optimization](https://mlsanity.com/paper/2209.07463/) (2022)
- [Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification](https://mlsanity.com/paper/2101.07361/) (2021)
- [Individually Fair Gradient Boosting](https://mlsanity.com/paper/2103.16785/) (2021)
- [Beyond "Fairness:" Structural (In)justice Lenses on AI for Education](https://mlsanity.com/paper/2105.08847/) (2021)
- [Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing](https://mlsanity.com/paper/2206.11423/) (2022)
- [A Human-Centric Take on Model Monitoring](https://mlsanity.com/paper/2206.02868/) (2022)
- [Survey on Fairness Notions and Related Tensions](https://mlsanity.com/paper/2209.13012/) (2022)
- [Fair Classifiers that Abstain without Harm](https://mlsanity.com/paper/2310.06205/) (2023)
- [What Constitutes a Less Discriminatory Algorithm?](https://mlsanity.com/paper/2412.18138/) (2024)
- [Fair Representations by Compression](https://mlsanity.com/paper/2105.14044/) (2021)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records](https://mlsanity.com/paper/2402.00955/) (2024)
- [Individually Fair Learning with One-Sided Feedback](https://mlsanity.com/paper/2206.04475/) (2022)

## Abstract (arXiv)

We study the problem of fair classification within the versatile framework of Dwork et al. [ITCS '12], which assumes the existence of a metric that measures similarity between pairs of individuals. Unlike earlier work, we do not assume that the entire metric is known to the learning algorithm; instead, the learner can query this arbitrary metric a bounded number of times. We propose a new notion of fairness called metric multifairness and show how to achieve this notion in our setting. Metric multifairness is parameterized by a similarity metric d on pairs of individuals to classify and a rich collection cal C of (possibly overlapping) "comparison sets" over pairs of individuals. At a high level, metric multifairness guarantees that similar subpopulations are treated similarly, as long as these subpopulations are identified within the class cal C.