Joshua Andrews
Joshua Andrews is a Data Scientist at Modern Hire. Working in R&D, Josh and his colleagues have developed fair machine learning models specifically designed to ensure fairness when selecting candidates for job opportunities, while simultaneously increasing prediction of job performance compared to traditional approaches. His work on fair machine learning for algorithmic hiring has been published with the Association for the Advancement of Artificial Intelligence (AAAI). Josh received his Ph.D. in Industrial/Organizational Psychology from North Carolina State University in 2019 and is currently a graduate student at the Georgia Institute of Technology school of Computer Science.
Artificial Intelligence (AI) has seen rapid expansion and integration into many sectors throughout our society. It’s helping medical professionals make more accurate diagnoses, it’s helping Engineers model and build better bridges, and now its helping selection scientists and HR professionals select the best available candidates for organizational positions. This session will provide attendees with fundamental knowledge concerning how AI is being applied to selection. Specifically, we will discuss applications of fair machine learning (ML), which has seen exponentially increasing research and public attention over the past several years. Based on research and best practices, we will discuss when, how, and why biased data impacts AI/ML modeling. With this knowledge, we cover some potential pitfalls and/or miss-applications of AI models that inadvertently lead to negative outcomes or potential liability.
If you are interested in learning more about the power of AI or about its potential pitfalls, we believe you will find this session refreshing and informative. Our goal for this session is to help attendees gain a comfortable understanding of AI applications in organizational selection. Attendees will learn some fundamentals of AI/ML so that they may better understand what signs HR professionals should look for when assessing an AI tool’s utility for their organization.
Attendees will leave the session with insight into the following:
How and where can AI be useful to my organization’s HR and selection needs?
What data should and should not be included in my selection procedures?
What are common “application pitfalls” I should avoid?
How can I tell if I am being oversold on something being AI?
How can I assess if an AI product is working/functioning the way it should? Is it meeting my organization’s needs?
WELLBEING
DIVERSITY
PURPOSE
CONNECTIVITY
