Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing

Abstract

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.

arXiv commentAccepted to AAAI/ACM AI Ethics and Society conference 2020

References in corpus (3)

• Racial categories in machine learning
• Deep Learning for Face Recognition: Pride or Prejudiced?
• Oxford Handbook on AI Ethics Book Chapter on Race and Gender

Cited by in corpus (11)

• Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem
• Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
• "It's Just Part of Me:" Understanding Avatar Diversity and Self-presentation of People with Disabilities in Social Virtual Reality
• Robots Enact Malignant Stereotypes
• Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention
• Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"
• Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities
• Some Challenges in Monitoring Epidemics
• Study on the Fairness of Speaker Verification Systems on Underrepresented Accents in English
• The Work of Art in an Age of Mechanical Generation
• A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild