# Identification and Inference for Algorithmic Frontiers with Selective Labels

arXiv: https://arxiv.org/abs/2606.14977 · HTML: https://mlsanity.com/paper/2606.14977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 57 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [A Convex Framework for Fair Regression](https://mlsanity.com/paper/1706.02409/) (2017)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier](https://mlsanity.com/paper/2206.00074/) (2022)
- [Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding](https://mlsanity.com/paper/2212.09844/) (2022)
- [The Statistical Fairness-Accuracy Frontier](https://mlsanity.com/paper/2508.17622/) (2025)
- [Inference for an Algorithmic Fairness-Accuracy Frontier](https://mlsanity.com/paper/2402.08879/) (2024)
- [Testing the Fairness-Accuracy Improvability of Algorithms](https://mlsanity.com/paper/2405.04816/) (2024)

## Abstract (arXiv)

This paper provides identification results to characterize a fairness-accuracy (FA) frontier, and statistical inference tools to test hypotheses and build a confidence set for the FA-frontier, when outcomes are observed only for selected individuals. When the selection process is unrestricted but loss is measured in specific ways, we provide a characterization of the sharp identification region of the FA-frontier. When the observed distribution rules out frontier membership for a binary status quo algorithm, we construct a single alternative algorithm that improves both groups' accuracy without worsening fairness, uniformly over all admissible distributions of missing labels. For any loss functions, under selection on observables and strict overlap assumptions, we obtain point identification and propose a debiased machine learning estimator, derive its asymptotic distribution, and show how this can be used to carry out inference for the FA-frontier.