# Robustness Disparities in Commercial Face Detection

arXiv: https://arxiv.org/abs/2108.12508 · HTML: https://mlsanity.com/paper/2108.12508/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 68 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 28 of 28)

- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Learning Adversarially Fair and Transferable Representations](https://mlsanity.com/paper/1802.06309/) (2018)
- [Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation](https://mlsanity.com/paper/1911.11834/) (2019)
- [Fairness without Demographics through Adversarially Reweighted Learning](https://mlsanity.com/paper/2006.13114/) (2020)
- [Empirical Risk Minimization under Fairness Constraints](https://mlsanity.com/paper/1802.08626/) (2018)
- [Censoring Representations with an Adversary](https://mlsanity.com/paper/1511.05897/) (2015)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [Minimax Pareto Fairness: A Multi Objective Perspective](https://mlsanity.com/paper/2011.01821/) (2020)
- [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition](https://mlsanity.com/paper/2101.07922/) (2021)
- [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://mlsanity.com/paper/2104.02821/) (2021)
- [Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning](https://mlsanity.com/paper/2006.12621/) (2020)
- [Racial categories in machine learning](https://mlsanity.com/paper/1811.11668/) (2018)
- [A Step Toward More Inclusive People Annotations for Fairness](https://mlsanity.com/paper/2105.02317/) (2021)
- [FNNC: Achieving Fairness through Neural Networks](https://mlsanity.com/paper/1811.00247/) (2018)
- [Google's Cloud Vision API Is Not Robust To Noise](https://mlsanity.com/paper/1704.05051/) (2017)
- [Intra-Processing Methods for Debiasing Neural Networks](https://mlsanity.com/paper/2006.08564/) (2020)
- [Minimax Group Fairness: Algorithms and Experiments](https://mlsanity.com/paper/2011.03108/) (2020)
- [On the Robustness of Face Recognition Algorithms Against Attacks and Bias](https://mlsanity.com/paper/2002.02942/) (2020)
- [Can we still avoid automatic face detection?](https://mlsanity.com/paper/1602.04504/) (2016)
- [Exploring a Makeup Support System for Transgender Passing based on Automatic Gender Recognition](https://mlsanity.com/paper/2103.04544/) (2021)

## Built on by (most cited first, 11 of 11)

- [Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations](https://mlsanity.com/paper/2205.07277/) (2022)
- [SoK: Anti-Facial Recognition Technology](https://mlsanity.com/paper/2112.04558/) (2021)
- [A Deep Dive into Dataset Imbalance and Bias in Face Identification](https://mlsanity.com/paper/2203.08235/) (2022)
- [Racial Bias within Face Recognition: A Survey](https://mlsanity.com/paper/2305.00817/) (2023)
- [Comparing Human and Machine Bias in Face Recognition](https://mlsanity.com/paper/2110.08396/) (2021)
- [HateProof: Are Hateful Meme Detection Systems really Robust?](https://mlsanity.com/paper/2302.05703/) (2023)
- [Explaining Bias in Deep Face Recognition via Image Characteristics](https://mlsanity.com/paper/2208.11099/) (2022)
- [Robustness Disparities in Face Detection](https://mlsanity.com/paper/2211.15937/) (2022)
- [Are Commercial Face Detection Models as Biased as Academic Models?](https://mlsanity.com/paper/2201.10047/) (2022)
- [Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints](https://mlsanity.com/paper/2409.16382/) (2024)
- [How Technology Impacts and Compares to Humans in Socially Consequential Arenas](https://mlsanity.com/paper/2211.03554/) (2022)

## Abstract (arXiv)

Facial detection and analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade. Critiques that focus on system performance analyze disparity of the system's output, i.e., how frequently is a face detected for different Fitzpatrick skin types or perceived genders. However, we focus on the robustness of these system outputs under noisy natural perturbations. We present the first of its kind detailed benchmark of the robustness of three such systems: Amazon Rekognition, Microsoft Azure, and Google Cloud Platform. We use both standard and recently released academic facial datasets to quantitatively analyze trends in robustness for each. Across all the datasets and systems, we generally find that photos of individuals who are older, masculine presenting, of darker skin type, or have dim lighting are more susceptible to errors than their counterparts in other identities.