# Measuring Social Biases of Crowd Workers using Counterfactual Queries

arXiv: https://arxiv.org/abs/2004.02028 · HTML: https://mlsanity.com/paper/2004.02028/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [PASS: An ImageNet replacement for self-supervised pretraining without humans](https://mlsanity.com/paper/2109.13228/) (2021)
- [The Data-Production Dispositif](https://mlsanity.com/paper/2205.11963/) (2022)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?](https://mlsanity.com/paper/2109.08131/) (2021)
- [Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation](https://mlsanity.com/paper/2402.06811/) (2024)
- [Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions](https://mlsanity.com/paper/2202.03734/) (2022)

## Abstract (arXiv)

Social biases based on gender, race, etc. have been shown to pollute machine learning (ML) pipeline predominantly via biased training datasets. Crowdsourcing, a popular cost-effective measure to gather labeled training datasets, is not immune to the inherent social biases of crowd workers. To ensure such social biases aren't passed onto the curated datasets, it's important to know how biased each crowd worker is. In this work, we propose a new method based on counterfactual fairness to quantify the degree of inherent social bias in each crowd worker. This extra information can be leveraged together with individual worker responses to curate a less biased dataset.