GenderRobustness: Robustness of Gender Detection in Facial Recognition Systems with variation in Image Properties

• URL: http://arxiv.org/abs/2011.10472v2
• Date: Thu, 26 Nov 2020 22:18:15 GMT
• Title: GenderRobustness: Robustness of Gender Detection in Facial Recognition Systems with variation in Image Properties
• Authors: Sharadha Srinivasan, Madan Musuvathi
• Abstract summary: There have been increasing accusations on artificial intelligence systems and algorithms of computer vision of possessing implicit biases. One such class of systems where bias is said to exist is facial recognition systems, where bias has been observed on the basis of gender, ethnicity, skin tone and other facial attributes. Developers of these systems to ensure that the bias is kept to a bare minimum or ideally non-existent.
• Score: 0.5330240017302619
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In recent times, there have been increasing accusations on artificial intelligence systems and algorithms of computer vision of possessing implicit biases. Even though these conversations are more prevalent now and systems are improving by performing extensive testing and broadening their horizon, biases still do exist. One such class of systems where bias is said to exist is facial recognition systems, where bias has been observed on the basis of gender, ethnicity, skin tone and other facial attributes. This is even more disturbing, given the fact that these systems are used in practically every sector of the industries today. From as critical as criminal identification to as simple as getting your attendance registered, these systems have gained a huge market, especially in recent years. That in itself is a good enough reason for developers of these systems to ensure that the bias is kept to a bare minimum or ideally non-existent, to avoid major issues like favoring a particular gender, race, or class of people or rather making a class of people susceptible to false accusations due to inability of these systems to correctly recognize those people.

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