# Algorithms that "Don't See Color": Comparing Biases in Lookalike and Special Ad Audiences

arXiv: https://arxiv.org/abs/1912.07579 · HTML: https://mlsanity.com/paper/1912.07579/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?](https://mlsanity.com/paper/2302.14063/) (2023)
- [Fair Algorithm Design: Fair and Efficacious Machine Scheduling](https://mlsanity.com/paper/2204.06438/) (2022)
- [Advancing Fairness in Natural Language Processing: From Traditional Methods to Explainability](https://mlsanity.com/paper/2410.12511/) (2024)

## Abstract (arXiv)

Researchers and journalists have repeatedly shown that algorithms commonly used in domains such as credit, employment, healthcare, or criminal justice can have discriminatory effects. Some organizations have tried to mitigate these effects by simply removing sensitive features from an algorithm's inputs. In this paper, we explore the limits of this approach using a unique opportunity. In 2019, Facebook agreed to settle a lawsuit by removing certain sensitive features from inputs of an algorithm that identifies users similar to those provided by an advertiser for ad targeting, making both the modified and unmodified versions of the algorithm available to advertisers. We develop methodologies to measure biases along the lines of gender, age, and race in the audiences created by this modified algorithm, relative to the unmodified one. Our results provide experimental proof that merely removing demographic features from a real-world algorithmic system's inputs can fail to prevent biased outputs. As a result, organizations using algorithms to help mediate access to important life opportunities should consider other approaches to mitigating discriminatory effects.