Study finds Amazon Rekognition suffers from racial and gender bias
An MIT Media Lab study concluded that Amazon's Rekognition facial recognition system performed worse when identifying an individual’s gender if they were female or darker-skinned. The MIT researchers compared tools from five companies, including Microsoft and IBM, and found that Rekognition performed the worst when it came to recognising women with darker skin, with an error rate of 31.37 percent. It also mistook women for men 19 percent of the time. Amazon claimed the research was misleading as the researchers had not tested the most recent version of Rekognition, and that the gender identification test was facial analysis (which spots expressions and characteristics like facial hair) rather than facial identification (which matches scanned faces to mugshots). In their paper, the researchers also argued that issues other than algorithmic fairness should be considered. 'The potential for weaponization and abuse of facial analysis technologies cannot be ignored nor the threats to privacy or breaches of civil liberties diminished even as accuracy disparities decrease,' they wrote. System 🤖 Amazon Rekognition 🔗 Operator: MIT Media Lab , Joy Buolamwini, Deborah Raji Developer: Amaz o n/AWS Country: USA Sector: Education Purpose: Identify individuals Technology: Facial recognition Issue: Bias/discrimination - racial, gen der; Dual/multi-use
- Date it happened
- 2019-01-01
- Organisation involved
- MIT Media Lab, Joy Buolamwini, Deborah Raji
- Product, system or model
- Amazon Rekognition
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