Use of algorithms in hiring, promotions, training and exit interviews is booming. ILR School faculty will teach “Algorithms at Work” May 4-5 at the Cornell Tech campus in New York City to help workplace professionals optimize their use of the popular, yet controversial, tool.

Algorithms are used across the employment life cycle for everything from scanning piles of resumes to find the perfect candidate, to predicting employee performance, to calculating the optimal retirement date to maximize retiree income.

This immersive workshop will help workplace professionals understand how algorithms work and how they can be effectively used as a decision-support tool.

Participants will also examine the ethical and legal questions that are becoming more prevalent as the use of algorithms expands. Participants do not need prior statistical training to be successful in the workshop.

“The aim of the course is to equip business leaders, in-house counsel and human resource managers with the analytical tools to evaluate automated algorithms for use in the workplace and to understand both their utility and limitations,” said Ifeoma Ajunwa, assistant professor of organizational behavior and adjunct Cornell Law School faculty member.

More information about the workshop is available by viewing this video.

Faculty teaching the course are Ajunwa; Martin T. Wells, the Charles A. Alexander Professor of Statistical Sciences; and M. Elizabeth Karns, senior lecturer in social statistics.

An algorithm – any set of instructions done repeatedly – can use machine learning or artificial intelligence to make predictions. “But it doesn’t mean those predictions are useful,” Wells said.

Ajunwa said the mathematical tools can avert some human bias in decisions, but bias and unethical practices can inadvertently be built into them. While some software vendors are carefully testing their products, the responsibility for ensuring algorithms aren’t discriminatory lies with human resources professionals and businesses, Ajunwa said. She has proposed continual audits of hiring algorithms.

Auditing of results might be needed on a continual basis, similar to the financial model whereby businesses do an audit every year. The course will give participants the tools to understand how algorithms and machine learning work, and how audits can be done to evaluate compliance, Ajunwa said.

Mary Catt is assistant director of communications at the ILR School.