Can Pre-hire Talent Assessments Be a Part of a Predictive Talent Acquisition Strategy? Over the past 30 years, businesses have spent billions on talent assessments. Many of these are now being used to understand job candidates. Increasingly, businesses are asking how (or if) a predictive talent acquisition strategy can include the use of pre-hire assessments? As costs of failed new hires continue to rise, recruiters and hiring managers are looking for any kind of pre-hire information to increase the probability of making a great hire.
Note: Thanks to Dr. Vivian Woo and Dr. Daniel Meltzer for their helpful feedback AI has been a hot topic in business over the last few years. The automation movement has been changing how we shop, work, and live our day-to-day lives, from self-checkout lanes to new phone apps. AI and automation also affect our hiring practices. In the human capital management world, AI/automation has become the topic du jour of many conferences and a variety of newspaper articles. Some recent Washington Post articles about the use of AI in hiring have caught the attention of many in the HR world, who may not be aware of how automated hiring practices are being implemented in organizations.
There has been rapidly growing interest in the use of algorithms for employment assessment, especially as a means to address or mitigate bias in hiring. Yet, to date, little is known about how these methods are being used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and assess the claims and practices of companies offering algorithms for employment assessment, using a methodology that can be applied to evaluate similar applications and issues of bias in other domains. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their techniques for detecting and mitigating bias. We find that companies' formulation of "bias" varies, as do their approaches to dealing with it. We also discuss the various choices vendors make regarding data collection and prediction targets, in light of the risks and trade-offs that these choices pose. We consider the implications of these choices and we raise a number of technical and legal considerations.
Whether as an interviewer or interviewee, it is a well-known reality that the hiring process is fraught with inconsistency and inherent human bias. Finance and marketing are among the industries leveraging huge data sets to make more predictive decisions. So with data collection at historically unprecedented levels, it is time to transform candidate hiring and assessment as well. Consider the classic pre-hire assessment: 100 questions that seem repetitive to the test taker. Yet, to the assessment creator, this is the bare minimum required to make an accurate prediction about future performance, as each new data point decreases the likelihood of a mistake and increases the predictive power of the assessment.