Nathaniel M. Glasser, Member of the Firm in the Employment, Labor & Workforce Management practice, in the firm’s Washington, DC, office, was quoted in Law360 Employment Authority, in “3 Tips for Using Tech in Equal Pay Compliance,” by Daniela Porat. (Read the full version – subscription required.)

Following is an excerpt:

Technological advancements and the advent of smarter artificial intelligence hold both promise and peril for equal pay, requiring employers to deploy these tools carefully by using technology to spot disparities or by designing fair algorithms. States and cities across the country are adopting stronger equal pay laws — be it requiring employers to post salary ranges in job postings or limiting employers' ability to defend a pay disparity to job-related reasons. This evolution in the equal pay landscape runs parallel to an increased awareness of the power and pitfalls of technology to confront discrimination. …

Here, Law360 explores three tips for employers to keep in mind as they evaluate how to incorporate technology into their equal pay compliance.

Use Tech To Find Pay Red Flags …

For example, a potential benefit of using AI is that it can provide more than a simple one-time snapshot of an organization's compensation scheme, said Nathaniel Glasser, a member of management-side firm Epstein Becker & Green PC.

"In theory, given the speed that these AI-powered tools can conduct these analyses, employers can view and analyze their compensation data much more closely to real time," he said. …

Beware Publicly Available Salary Data

One of the major trends in the equal pay landscape deals with salary history bans — a counterpart to the pay transparency legislation sweeping the country.

Salary history bans generally bar employers from inquiring about a job candidate's compensation history or relying on a person's salary history for setting compensation decisions.

Some tools may be able to pull compensation information from publicly available data about job applicants and that could pose a problem, Glasser said.

"Employers have to understand how the tool works, the type of data that it will analyze in order to make its decision or recommendations," he said.

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