Applying Machine Learning and Artificial Intelligence to Personnel Selection and Staffing: Efficacy and Ethics

Business in Society; Artificial Intelligence/Robotics; Communication; Ethics; data science

The goal of the study is to use machine learning/AI to quantify more accurate algorithms for scoring interviews, the goal is to develop predictive algorithms for scoring interviews for future success.

Research Interests
  • Ethics in business/ethics in marketing
  • Data Mining
  • Machine Learning, Text Mining, Information Retrieval
  • Communications

Organizations are rapidly adopting machine learning and artificial intelligence approaches to personnel selection and staffing. In some cases, firms are applying artificial intelligence tools as part of their screening process to assess short videos of candidates, before any human being views the applicant.  With or without a human interviewer/assessor, can machine learning and artificial intelligence approaches match or exceed the assessment capabilities of human beings? 

Calls for interdisciplinary research in the domain of personnel selection and staffing including new testing and assessment methods; reliability and validity of tools; interviewing, detecting, and remediating adverse impacts including bias; and interpretability of algorithms highlight the need for strong empirical studies. 

In addition to questions about efficacy, numerous ethical questions concerning the use of machine learning and artificial intelligence have arisen in this domain, where practice is ahead of science. Are these approaches scientifically and ethically sound? What are the effects on people and organizations? How can these tools be used virtuously and justly? What are the risks, and how can they be mitigated?

This project addresses the enormous opportunity to improve the body of knowledge in industrial and organizational psychology, organizational behavior, and human resources management, and brings together a team of researchers suited to the task. The project will  draw from a large and detailed repository of candidates and recorded interviews including audio, visual, and transcription of real-world job candidates whose interview and employment outcomes and other characteristics are known. 

Funding will be used to support a PhD candidate or postdoctoral fellow, hire and train student research assistants, and meet expenses including hiring of outside expert interviewers/assessors and investment in technical tools, equipment, or computing needs. 

Desired outcomes

·      Using machine learning and artificial intelligence approaches, quantify and develop more accurate algorithms including:

       o   lexical features regarding interview content, speaking fluency, and articulation

       o   prosody, or speaking style, including pitch and vocal intensities, in relation to answers to interview questions and use of expressive language

      o   facial features and expressions, including hand and head gestures, posture, gazes, and overall visual motion. The computational models should include data on features such as candidate ethnicity, age, gender, degree of physical attractiveness, etc., which can help to reduce bias.

·      Compare and evaluate machine learning and artificial intelligence methods vs. more traditional (and often more expensive) methods like manual qualitative coding and expert assessments of interviews.

·      Explore and address key ethical/philosophical questions about the development and use of machine learning and artificial intelligence methods in personnel selection and staffing.

·      The ultimate intent of the study is to develop and evaluate predictive algorithms for interview settings and explore the ethics around these approaches, using the results to prepare firms, interviewers, and candidates to be more effective while reducing the effects of bias and non-job-related factors in hiring decisions.