Tool helps clear biases from computer vision

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The Journal of Theoretical and Computational Science aims to spread knowledge and promote discussion through the publication of peer-reviewed, high quality research papers on all topics related to Modern Scientific Techniques. The open access journal is published by Longdom Publishing who hosts open access peer-reviewed journals as well as organizes conferences that hosts the work of researchers in a manner that exemplifies the highest standards in research integrity.

Researchers have developed a tool that flags potential biases in sets of images used to train artificial intelligence (AI) systems. The work is part of a larger effort to remedy and prevent the biases that have crept into AI systems that influence everything from credit services to courtroom sentencing programs.

Although the sources of bias in AI systems are varied, one major cause is stereotypical images contained in large sets of images collected from online sources that engineers use to develop computer vision, a branch of AI that allows computers to recognize people, objects and actions. Because the foundation of computer vision is built on these data sets, images that reflect societal stereotypes and biases can unintentionally influence computer vision models.

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Joseph Marreddy
Managing Editor
Journal of Theoretical and Computational Science
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