Machine Learning Bias in Computer Vision: Why do I have to care?

Camila Laranjeira, V. F. Mota, J. A. D. Santos
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引用次数: 1

Abstract

Machine Learning bias is an issue with two main disadvantages. It compromises the quantitative performance of a system, and depending on the application, it may have a strong impact on society from an ethical viewpoint. In this work we inspect the literature on Computer Vision focusing on human-centered applications such as computer-aided diagnosis and face recognition to outline several forms of bias, bringing study cases for a more thorough inspection of how this issue takes form in the field of machine learning applied to images. We conclude with proposals from the literature on how to solve, or at least minimize, the impacts of bias.
计算机视觉中的机器学习偏差:为什么我必须关心?
机器学习偏差有两个主要缺点。它损害了系统的定量性能,并且根据应用程序的不同,从道德的角度来看,它可能对社会产生强烈的影响。在这项工作中,我们检查了关于计算机视觉的文献,重点关注以人为中心的应用,如计算机辅助诊断和人脸识别,概述了几种形式的偏见,带来了研究案例,以便更彻底地检查这个问题在应用于图像的机器学习领域是如何形成的。最后,我们从文献中提出了如何解决或至少最小化偏见影响的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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