Dan Putka, Ph.D., recently joined a new task force, Artificial Intelligence (AI)-Based Personnel Assessment and Prediction, convened by the Society for Industrial and Organizational Psychology (SIOP).
The use of AI-based assessment technologies has expanded dramatically in recent years, and encompasses information pulled from social media, personal characteristics related to vocal and facial features, responses to nontraditional assessments including “serious games,” and trace data, such as reaction times and app clicks. Use of these new assessment technologies is often rooted in machine learning-based algorithms designed to predict important criteria in the talent management arena such as future job performance, knowledge and proficiency levels, and turnover intentions and behavior.
Despite their promise, the increasing use of AI-based assessments has raised concerns about the potential for bias and unfair discrimination impacting classes protected by Equal Employment Opportunity (EEO) laws and regulations, including race, ethnicity, gender, and age. These concerns are reflected in pending and recently passed legislation setting guardrails around the use of AI in hiring contexts at the local and state levels.
The task force’s general charge is to promote the visibility of scientific research relevant to the effective use of AI-based assessments and to formulate guidelines and best practices involving their use within organizations. The task force also will help set the AI-focused research agenda by highlighting knowledge gaps and potentially fruitful research strategies and work to educate the public about the role of industrial-organizational psychology in promoting fair, effective talent management involving the use of AI.
“Dan’s expertise in machine learning and cutting-edge prediction methods, including how to best apply these methodologies in practice, is very unique in our field” shared Steven Rogelberg, SIOP’s president. “I am thrilled that he agreed to join the task force.”
A HumRRO principal scientist, Putka’s work has helped numerous public and private sector organizations develop, evaluate, and implement assessments to both enhance their hiring and promotion processes and guide individuals toward career and job opportunities that fit them well. He has led research efforts demonstrating how the predictive power of methods grounded in machine learning can outperform those more commonly used in human capital practice, including this award-winning 2018 research published in the journal, Organizational Research Methods. He contributed to a National Academies of Sciences, Engineering, and Medicine report, Strengthening U.S. Air Force Human Capital Management, and recently served on the committee charged with updating SIOP’s Principles for the Validation and Use of Personnel Selection Procedures.