# Fairer and more accurate, but for whom?

arXiv: https://arxiv.org/abs/1707.00046 · HTML: https://mlsanity.com/paper/1707.00046/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2017.

## Built on by (most cited first, 13 of 13)

- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Characterizing Fairness Over the Set of Good Models Under Selective Labels](https://mlsanity.com/paper/2101.00352/) (2021)
- [Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables](https://mlsanity.com/paper/2207.13834/) (2022)
- [Algorithmic Fairness in Business Analytics: Directions for Research and Practice](https://mlsanity.com/paper/2207.10991/) (2022)
- [Robustness and Cybersecurity in the EU Artificial Intelligence Act](https://mlsanity.com/paper/2502.16184/) (2025)
- [Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence](https://mlsanity.com/paper/2103.00752/) (2021)
- [SoK: Machine Learning Governance](https://mlsanity.com/paper/2109.10870/) (2021)
- [Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy](https://mlsanity.com/paper/2305.01783/) (2023)
- [Locating disparities in machine learning](https://mlsanity.com/paper/2208.06680/) (2022)
- [A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution](https://mlsanity.com/paper/2410.21716/) (2024)
- [Interpretable Differencing of Machine Learning Models](https://mlsanity.com/paper/2306.06473/) (2023)
- [FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition](https://mlsanity.com/paper/2604.19357/) (2026)
- [Auditing Predictive Models for Intersectional Biases](https://mlsanity.com/paper/2306.13064/) (2023)

## Abstract (arXiv)

Complex statistical machine learning models are increasingly being used or considered for use in high-stakes decision-making pipelines in domains such as financial services, health care, criminal justice and human services. These models are often investigated as possible improvements over more classical tools such as regression models or human judgement. While the modeling approach may be new, the practice of using some form of risk assessment to inform decisions is not. When determining whether a new model should be adopted, it is therefore essential to be able to compare the proposed model to the existing approach across a range of task-relevant accuracy and fairness metrics. Looking at overall performance metrics, however, may be misleading. Even when two models have comparable overall performance, they may nevertheless disagree in their classifications on a considerable fraction of cases. In this paper we introduce a model comparison framework for automatically identifying subgroups in which the differences between models are most pronounced. Our primary focus is on identifying subgroups where the models differ in terms of fairness-related quantities such as racial or gender disparities. We present experimental results from a recidivism prediction task and a hypothetical lending example.