Massachusetts Institute of Technology and Stanford University researchers recently found that three commercial facial analysis programs were producing different results for different skin types and gender. They found that error rates for determining the gender of light-skinned men were 0.8 percent while error rates for darker-skinned women increased to up to 34 percent. Unfortunately, this is not the only example where AI seems to lose neutrality. Biased algorithms threaten to erode trust in AI platforms. Problems can arise when bias exists in the original data on which the algorithm is trained.

Massachusetts Institute of Technology and Stanford University researchers recently found that three commercial facial analysis programs were producing different results for different skin types and gender. They found that error rates for determining the gender of light-skinned men were 0.8 percent while error rates for darker-skinned women increased to up to 34 percent. Unfortunately, this is not the only example where AI seems to lose neutrality. Biased algorithms threaten to erode trust in AI platforms. Problems can arise when bias exists in the original data on which the algorithm is trained.