Roger Ibars

Last year we published a conceptual framing tool for identifying bias in AI, How to Recognize Exclusion in AI. It was a way to address an imminent concern in the tech industry: the emergence of gender, racial, and socioeconomic bias in seemingly harmless technological experiences. A year later, that framework still speaks to urgent issues. It calls on the industry to analyze the state of AI development and slow the astronomical pace driving AI into unpredictable corners of technology.

Can artificial intelligence be racist? Well, it depends. Let’s say you’re an African-American student at a school that uses facial recognition software. The school uses it to access the building and online homework assignments. But the software’s got a problem. Its makers used only light-skinned test subjects to train its algorithms. Your skin is darker, and the software has trouble recognizing you. Sometimes you’re late to class, or can’t get your assignments on time. Your grades suffer.