Michelle Lee is currently a Data Scientist at Atipica and is a Senior Data Analytics Instructor at Product School. Amazon was recently reported to have built a machine learning model aimed at ranking candidates for its open roles. However, the team building the tool was disbanded due to the biases learned by the model. For example, the model ranked candidates lower if their resumes included … Continue reading AI Bias in Recruiting: Why Does It Happen?
Gender inequity is an important topic in the tech industry. Despite its importance, well-designed studies, based on rich data are scarce in the public domain. Within firms, where the data is abundant, lack of a rigorous scientific framework prevents many HR departments from truly understanding the root-cause of any inequity that may exist, resorting to reliance on anecdotal evidence. The primary goal of this post … Continue reading A Framework to Assess Gender Inequity in Hiring using Data
Written by Prasanna Parasurama and originally published on Medium. Special thanks to Vaibhav Pahwa and Rubi Sanchez-Castro. The fruits of innovation in machine learning have barely reached the talent industry, much less recruiting teams. According to the Wall Street Journal, approximately 90% of companies use an Applicant Tracking System (ATS) that parses applicants’ resumes, extracts information and makes its content searchable. These tools, however, rely … Continue reading Hidden Diamonds: Using Skill Mapper to Find Compatible Talent