With the HR Technology Conference last week (see agenda here), it is exciting to see the growing tech and AI capabilities across various vendor platforms. One recruiting and hiring capability for HR technology is using AI, machine learning, and natural language processing to reduce a large pool of candidates to a select few who most closely match specified criteria for a position. But as I pointed out in a recent post, a study by Harvard Business School and Accenture, Hidden Workers: Untapped Talent, found that these technologies can exclude viable candidates from consideration. As firms seek the benefits of these technologies while reducing the risk, this MIT article provides a few ideas: one involves minimizing unintentional bias. It notes that while humans have preexisting biases in the hiring process (more than 180 biases), AI can be prone to algorithmic bias. The authors expand on the notion of Explainable and Transparent AI—where the methods or techniques used to arrive at conclusions can be easily explained and understood. Within this context, they provide 10 starter questions that practitioners can ask to vet vendors/providers of these services. Two questions include 3. How do you measure and mitigate that bias? How often do you test (weekly, monthly, quarterly)? 8. Can you explain why the solution recommended one candidate over another? Can you explain other types of results — for instance, why someone was ranked highly while someone else was not? A useful reference for decision-makers of these solutions.