Diversity and inclusion of many people in a population sample can help engineer the bias out of datasets on which AI systems rely. (Credit: Getty Images)
By John P. Desmond, AI Trends Editor
With AI systems today determining whether someone can get a job or a loan, it’s in the interest of the company running the AI system to make sure the underlying dataset is not so biased that it leads to errors in its conclusions.
Cases of biased data leading to biased results have been documented, such as in the research of Joy Buolamwini and Timnit Gebru, authors …
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