# How Costly is Noise? Data and Disparities in Consumer Credit

arXiv: https://arxiv.org/abs/2105.07554 · HTML: https://mlsanity.com/paper/2105.07554/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 79 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 5 of 5)

- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?](https://mlsanity.com/paper/1912.01094/) (2019)
- [Predicting Race and Ethnicity From the Sequence of Characters in a Name](https://mlsanity.com/paper/1805.02109/) (2018)
- [To Split or Not to Split: The Impact of Disparate Treatment in Classification](https://mlsanity.com/paper/2002.04788/) (2020)

## Built on by (most cited first, 8 of 8)

- [Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation](https://mlsanity.com/paper/2210.02516/) (2022)
- [Algorithmic Censoring in Dynamic Learning Systems](https://mlsanity.com/paper/2305.09035/) (2023)
- [Modelling the long-term fairness dynamics of data-driven targeted help on job seekers](https://mlsanity.com/paper/2208.08881/) (2022)
- [InfoGram and Admissible Machine Learning](https://mlsanity.com/paper/2108.07380/) (2021)
- [Fair Algorithm Design: Fair and Efficacious Machine Scheduling](https://mlsanity.com/paper/2204.06438/) (2022)
- [Robustness Evaluation and Adversarial Training of an Instance Segmentation Model](https://mlsanity.com/paper/2206.02539/) (2022)
- [De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data](https://mlsanity.com/paper/2405.00910/) (2024)
- [Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI](https://mlsanity.com/paper/2508.07872/) (2025)

## Abstract (arXiv)

We show that lenders face more uncertainty when assessing default risk of historically under-served groups in US credit markets and that this information disparity is a quantitatively important driver of inefficient and unequal credit market outcomes. We first document that widely used credit scores are statistically noisier indicators of default risk for historically under-served groups. This noise emerges primarily through the explanatory power of the underlying credit report data (e.g., thin credit files), not through issues with model fit (e.g., the inability to include protected class in the scoring model). Estimating a structural model of lending with heterogeneity in information, we quantify the gains from addressing these information disparities for the US mortgage market. We find that equalizing the precision of credit scores can reduce disparities in approval rates and in credit misallocation for disadvantaged groups by approximately half.