mlsanityData to 7 Oct 2026

Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing

arXiv 2001.00964 ↗First posted 3 Jan 2020cs.CYcited by 89

Not on the map. Why?

Abstract (arXiv)

Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect. This concern is even more salient while auditing biometric systems such as facial recognition, where the data is sensitive and the technology is often used in ethically questionable manners. We demonstrate a set of five ethical concerns in the particular case of auditing commercial facial processing technology, highlighting additional design considerations and ethical tensions the auditor needs to be aware of so as not exacerbate or complement the harms propagated by the audited system. We go further to provide tangible illustrations of these concerns, and conclude by reflecting on what these concerns mean for the role of the algorithmic audit and the fundamental product limitations they reveal.

Key facts. Cited by 89 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jan 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 20; 2023: 18; 2024: 12; 2025: 13; 2026: 3.

89 papers.

Sort:

By area:

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 123
  3. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
  4. 2021Towards Measuring Fairness in AI: the Casual Conversations Datasetcited by 80
  5. 2021Towards Unbiased and Accurate Deferral to Multiple Expertscited by 71
  6. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 59
  7. 2021Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Paperscited by 49
  8. 2021Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffscited by 48
  9. 2021Deep Learning for Micro-expression Recognition: A Surveycited by 43
  10. 2024Evolving Code with A Large Language Modelcited by 42
  11. 2022Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparitiescited by 27
  12. 2022Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognitioncited by 26
38 more
  1. 2023A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?cited by 25
  2. 2022Fairness Indicators for Systematic Assessments of Visual Feature Extractorscited by 24
  3. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  4. 2023Concrete Problems in AI Safety, Revisitedcited by 21
  5. 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
  6. 2021Technical Challenges for Training Fair Neural Networkscited by 20
  7. 2021Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systemscited by 20
  8. 2021A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communicationcited by 18
  9. 2023Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Thingscited by 17
  10. 2023Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Colorcited by 17
  11. 2021Measuring Hidden Bias within Face Recognition via Racial Phenotypescited by 16
  12. 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
  13. 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
  14. 2024Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?cited by 15
  15. 2022Robots Enact Malignant Stereotypescited by 14
  16. 2022Analyzing Fairness in Deepfake Detection With Massively Annotated Databasescited by 13
  17. 2025Benchmarking Foundation Models for Zero-Shot Biometric Taskscited by 13
  18. 2021AI Ethics Principles in Practice: Perspectives of Designers and Developerscited by 12
  19. 2022Fair SA: Sensitivity Analysis for Fairness in Face Recognitioncited by 11
  20. 2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvementscited by 11
  21. 2022Fairness and Bias in Robot Learningcited by 10
  22. 2022Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learningcited by 10
  23. 2023Racial Bias within Face Recognition: A Surveycited by 9
  24. 2021Two-Face: Adversarial Audit of Commercial Face Recognition Systemscited by 8
  25. 2022Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflowscited by 8
  26. 2025Discovering Forbidden Topics in Language Modelscited by 8
  27. 2021Detecting discriminatory risk through data annotation based on Bayesian inferencescited by 7
  28. 2025Review of Demographic Fairness in Face Recognitioncited by 7
  29. 2021Fairness Testing of Deep Image Classification with Adequacy Metricscited by 6
  30. 2022Adjusting for Bias with Procedural Datacited by 6
  31. 2022Does lossy image compression affect racial bias within face recognition?cited by 5
  32. 2022Fairness Increases Adversarial Vulnerabilitycited by 5
  33. 2024Law and the Emerging Political Economy of Algorithmic Auditscited by 5
  34. 2021AI and Blackness: Towards moving beyond bias and representationcited by 4
  35. 2022Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Imagescited by 3
  36. 2022Enhancing Fairness of Visual Attribute Predictorscited by 3
  37. 2022A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysiscited by 3
  38. 2023Uncovering Bias in Personal Informaticscited by 3

50 most cited of 89 All 89 (JSON)