# Who Decides if AI is Fair? The Labels Problem in Algorithmic Auditing

arXiv: https://arxiv.org/abs/2111.08723 · HTML: https://mlsanity.com/paper/2111.08723/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [From ImageNet to Image Classification: Contextualizing Progress on Benchmarks](https://mlsanity.com/paper/2005.11295/) (2020)

## Built on by (most cited first, 2 of 2)

- [What's the Meaning of Superhuman Performance in Today's NLU?](https://mlsanity.com/paper/2305.08414/) (2023)
- [SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata](https://mlsanity.com/paper/2311.17259/) (2023)

## Abstract (arXiv)

Labelled "ground truth" datasets are routinely used to evaluate and audit AI algorithms applied in high-stakes settings. However, there do not exist widely accepted benchmarks for the quality of labels in these datasets. We provide empirical evidence that quality of labels can significantly distort the results of algorithmic audits in real-world settings. Using data annotators typically hired by AI firms in India, we show that fidelity of the ground truth data can lead to spurious differences in performance of ASRs between urban and rural populations. After a rigorous, albeit expensive, label cleaning process, these disparities between groups disappear. Our findings highlight how trade-offs between label quality and data annotation costs can complicate algorithmic audits in practice. They also emphasize the need for development of consensus-driven, widely accepted benchmarks for label quality.