Facial Recognition Bias and the Need for Inclusive Data Sets

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Facial recognition technology has been skewed due to the lack of diversity in the datasets used. The gold standard database, LFW, primarily consisted of lighter-skinned individuals and males, leading to a false sense of progress. The researcher conducted MIT research to explore the impact of including more diverse demographics in the test. Using the Pilot Parliament's benchmark, which included more women and people of color, significant biases were discovered in gender and skin type across systems from IBM, Microsoft, Face Plus Plus, Amazon, and Clarify.

In this interview, Dr. Joy Buolamwini argues that algorithmic bias in AI systems poses risks to marginalized people. She challenges the assumptions of tech leaders who advocate for AI “alignment” and explains why some tech companies are hypocritical when it comes to addressing bias.

Dr. Joy Buolamwini is the founder of the Algorithmic Justice League and the author of “Unmasking AI: My Mission to Protect What Is Human in a World of Machines.”

Correction: Aza says that Sam Altman, the CEO of OpenAI, predicts superintelligence in four years. Altman predicts superintelligence in ten years.

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