# Metric Learning for Individual Fairness

arXiv: https://arxiv.org/abs/1906.00250 · HTML: https://mlsanity.com/paper/1906.00250/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2019.

## Built on by (most cited first, 30 of 52)

- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [Post-processing for Individual Fairness](https://mlsanity.com/paper/2110.13796/) (2021)
- [Teaching Humans When To Defer to a Classifier via Exemplars](https://mlsanity.com/paper/2111.11297/) (2021)
- [Individual Fairness Guarantees for Neural Networks](https://mlsanity.com/paper/2205.05763/) (2022)
- [Moral Machine or Tyranny of the Majority?](https://mlsanity.com/paper/2305.17319/) (2023)
- [An Algorithmic Framework for Bias Bounties](https://mlsanity.com/paper/2201.10408/) (2022)
- [Latent Space Smoothing for Individually Fair Representations](https://mlsanity.com/paper/2111.13650/) (2021)
- [What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective](https://mlsanity.com/paper/2206.04101/) (2022)
- [Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare](https://mlsanity.com/paper/2202.01906/) (2022)
- [Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders](https://mlsanity.com/paper/2205.08928/) (2022)
- [Statistical inference for individual fairness](https://mlsanity.com/paper/2103.16714/) (2021)
- [Individually Fair Gradient Boosting](https://mlsanity.com/paper/2103.16785/) (2021)
- [Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning](https://mlsanity.com/paper/2203.12748/) (2022)
- [Scaffolding Sets](https://mlsanity.com/paper/2111.03135/) (2021)
- [Multi-disciplinary fairness considerations in machine learning for clinical trials](https://mlsanity.com/paper/2205.08875/) (2022)
- [Individual Preference Stability for Clustering](https://mlsanity.com/paper/2207.03600/) (2022)
- [Learning Antidote Data to Individual Unfairness](https://mlsanity.com/paper/2211.15897/) (2022)
- [Societal Alignment Frameworks Can Improve LLM Alignment](https://mlsanity.com/paper/2503.00069/) (2025)
- [Individually Fair Ranking](https://mlsanity.com/paper/2103.11023/) (2021)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)
- [Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness](https://mlsanity.com/paper/2303.00673/) (2023)
- [Threshold-Consistent Margin Loss for Open-World Deep Metric Learning](https://mlsanity.com/paper/2307.04047/) (2023)
- [Is this model reliable for everyone? Testing for strong calibration](https://mlsanity.com/paper/2307.15247/) (2023)
- [Individually Fair Learning with One-Sided Feedback](https://mlsanity.com/paper/2206.04475/) (2022)
- [iFlipper: Label Flipping for Individual Fairness](https://mlsanity.com/paper/2209.07047/) (2022)
- [Human-Guided Fair Classification for Natural Language Processing](https://mlsanity.com/paper/2212.10154/) (2022)
- [What is Fair? Defining Fairness in Machine Learning for Health](https://mlsanity.com/paper/2406.09307/) (2024)
- [EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders](https://mlsanity.com/paper/2407.11442/) (2024)
- [Learning to Generate Fair Clusters from Demonstrations](https://mlsanity.com/paper/2102.03977/) (2021)
- [How to Design Robust Algorithms using Noisy Comparison Oracle](https://mlsanity.com/paper/2105.05782/) (2021)

## Abstract (arXiv)

There has been much discussion recently about how fairness should be measured or enforced in classification. Individual Fairness [Dwork, Hardt, Pitassi, Reingold, Zemel, 2012], which requires that similar individuals be treated similarly, is a highly appealing definition as it gives strong guarantees on treatment of individuals. Unfortunately, the need for a task-specific similarity metric has prevented its use in practice. In this work, we propose a solution to the problem of approximating a metric for Individual Fairness based on human judgments. Our model assumes that we have access to a human fairness arbiter, who can answer a limited set of queries concerning similarity of individuals for a particular task, is free of explicit biases and possesses sufficient domain knowledge to evaluate similarity. Our contributions include definitions for metric approximation relevant for Individual Fairness, constructions for approximations from a limited number of realistic queries to the arbiter on a sample of individuals, and learning procedures to construct hypotheses for metric approximations which generalize to unseen samples under certain assumptions of learnability of distance threshold functions.