Open AccessPosted Content
Deep Learning for Face Recognition: Pride or Prejudiced?
TLDR
A better understanding of state-of-the-art deep learning networks would enable researchers to address the given challenge of bias in AI, and develop fairer systems.Abstract:
Do very high accuracies of deep networks suggest pride of effective AI or are deep networks prejudiced? Do they suffer from in-group biases (own-race-bias and own-age-bias), and mimic the human behavior? Is in-group specific information being encoded sub-consciously by the deep networks?
This research attempts to answer these questions and presents an in-depth analysis of `bias' in deep learning based face recognition systems This is the first work which decodes if and where bias is encoded for face recognition Taking cues from cognitive studies, we inspect if deep networks are also affected by social in- and out-group effect Networks are analyzed for own-race and own-age bias, both of which have been well established in human beings The sub-conscious behavior of face recognition models is examined to understand if they encode race or age specific features for face recognition Analysis is performed based on 36 experiments conducted on multiple datasets Four deep learning networks either trained from scratch or pre-trained on over 10M images are used Variations across class activation maps and feature visualizations provide novel insights into the functioning of deep learning systems, suggesting behavior similar to humans It is our belief that a better understanding of state-of-the-art deep learning networks would enable researchers to address the given challenge of bias in AI, and develop fairer systemsread more
Citations
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Journal ArticleDOI
Demographic Bias in Biometrics: A Survey on an Emerging Challenge
TL;DR: The main contributions of this article are an overview of the topic of algorithmic bias in the context of biometrics, a comprehensive survey of the existing literature on biometric bias estimation and mitigation, and a discussion of the pertinent technical and social matters.
Proceedings ArticleDOI
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
Inioluwa Deborah Raji,Timnit Gebru,Margaret Mitchell,Joy Buolamwini,Joonseok Lee,Emily Denton +5 more
TL;DR: A set of five ethical concerns in the particular case of auditing commercial facial processing technology are demonstrated, highlighting additional design considerations and ethical tensions the auditor needs to be aware of so as not to exacerbate or complement the harms propagated by the audited system.
Journal ArticleDOI
Issues Related to Face Recognition Accuracy Varying Based on Race and Skin Tone
TL;DR: Using two different deep convolutional neural network face matchers, it is shown that for a fixed decision threshold, the African-American image cohort has a higher false match rate (FMR), and the Caucasian cohort hasA higher false nonmatch rate.
Posted Content
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
Inioluwa Deborah Raji,Timnit Gebru,Margaret Mitchell,Joy Buolamwini,Joonseok Lee,Emily Denton +5 more
TL;DR: In this paper, the authors 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.
Proceedings ArticleDOI
Face Recognition: Too Bias, or Not Too Bias?
TL;DR: In this article, the authors reveal critical insights into problems of bias in state-of-the-art facial recognition (FR) systems using a novel Balanced Faces In the Wild (BFW) dataset: data balanced for gender and ethnic groups.
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