Report: Artificial Intelligence in HR: How to Address Racial Biases and Algorithmic Discrimination in HR? | ENAR

Artificial intelligence and analytics continue to permeate most areas of HR, ranging from recruiting to compensation. In fact, a few reports suggest that over 250 different commercial AI-based HR tools exist today. And while these tools are incredibly powerful and produce many benefits (e.g., reduce time in screening many applicants during the selection process), they also come with risks; one of those risks is algorithmic bias, where AI-based algorithms can amplify institutional and historical biases that discriminate based on race, gender, and class. For example, a new HBR article notes how Amazon stopped using a resume screening tool built by its engineers because it was biased against women. On the recruiting front, a social media ad for Science, Technology, Engineering and Math (STEM) field opportunities that had been carefully designed to be gender-neutral was shown disproportionately to men by an algorithm designed to maximize value for recruiters’ ad budgets, because women are generally more responsive to advertisements and thus ads shown to them are more expensive. As HR leaders seek to reap the benefits of AI platforms while reducing risks, this 28-page report provides several insights. Page 10 begins a section that shows how bias emerges in AI-based HR tools, such as chatbots, video interviews, targeted job advertisements, performance evaluation, etc. Page 16 begins to discuss bias mitigation techniques.