[Podcast] AI in the Workplace: What Employers need to Know

In the latest episode of The Proskauer Brief, we are joined by Guy Brenner, partner at Proskauer in Washington, D.C., who is also co-head of Proskauer’s Counseling, Training & Pay Equity Group, as well as Jonathan Slowik, senior Counsel, Labor & Employment, from the firm’s Los Angeles office. In part one of the insightful artificial intelligence series we explore what employers should know about using AI to make employment decisions such as hiring and promotion. Tune in as we break down key considerations and best practices for navigating the evolving landscape of AI in the workplace and provide essential tips that can enhance your approach to talent management.

Listen to the podcast.

Guy Brenner: Welcome to The Proskauer Brief: Hot Topics in Labor and Employment Law. Jonathan Slowik, senior Counsel, Labor & Employment, is a partner at Proskauer in the Washington, D.C. office. Guy Brenner, a Partner in Proskauer’s Employment Litigation & Counseling Group, will be joining me. This is the first part of a series that will explain what employers need to know when it comes to using artificial intelligence (AI) to make employment decisions such as hiring and promotion. Jonathan, thank you for joining me today.

Jonathan Slowik: It’s great to be here, Guy.

Guy Brenner: Over the course of the next few episodes, we’re going to be taking a deep dive into three potential pitfalls of using AI when it comes to hiring and promotions, which we’ll get into in a little bit. Before we move on, I think we should first define AI and look at some of the many practical applications that it provides for employers and HR professionals. Jonathan, why not start by giving us a simple definition of AI?

Jonathan Slowik : Sure, Guy. AI is the simulation of human intellect in machines that have been programmed to learn and think like humans. AI is a broad term that encompasses many technologies and applications which enable machines to perform tasks normally requiring human intelligence. Exhibit A–do you know where I got that definition of AI?

Guy Brenner: I’m going to take a wild guess, and I’m going to say it was ChatGPT.

Jonathan Slowik: Your wild guess is spot on.

Guy Brenner: But it wouldn’t be an AI podcast without some dramatic reveal that the thing we just told you was actually written by a robot.

Jonathan Slowik: It certainly wouldn’t. If you prefer a definition that was written by a human, there’s a pretty good one in the National Artificial Intelligence Initiative Act of 2020, which defines AI as “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.”

Guy Brenner: All right. In my mind, AI is a computer program or tool capable of doing amazing things, which are similar to or even close to thinking. We can do things I didn’t believe were possible or things from science fiction. And I think what we’ve discovered is that at least, for example, ChatGPT with each successive version is becoming more and more powerful.

Jonathan Slowik: Sure.

Guy Brenner: All right. Now that we have defined AI, let’s look at some of the applications for the technology within the hiring and HR context. Jonathan, why don’t you provide some examples.

Jonathan Slowik: Yeah. One problem we hear about a lot is hiring managers being overwhelmed by the number of applications they receive. The technology has made it easier to apply for a job. Many applicants can now apply for jobs online. Some platforms, like LinkedIn and Indeed, allow applicants to submit their resumes in bulk. This Reddit comment summarizes what many hiring managers are feeling these days. “As a manager, I cannot agree more with the insane volume of ineligible and/or unqualified applicants who apply for jobs.” It has made the hiring process so much more difficult as some people just seem to hit the LinkedIn apply button for anything that sounds interesting.” Guy, what’s the use case for AI in this circumstance?

Guy Brenner: And I think a lot of our listeners can relate to that challenge. There are companies that offer AI resume scans that can help employers prioritize applications or even screen out some applications. Some of these products claim to remove bias by using a dispassionate AI bot instead of a person with their inherent biases. One such developer claims: “AI screening goes past keywords and identifies candidates based on aptitude.” This feature removes conscious and unconscious human bias from the evaluation. Human judgment can be affected by identifying information such as gender, location, age and marital status. Other factors include education, career status, disability and resume photos. By sticking to a fixed set of criteria, AI increases the accuracy of applicant selection during the screening process.”

Another way hiring entities are using AI is having chatbots engage with potential candidates before applying. This use case is based on the theory that chatbots will create a more informed pool of applicants and may even steer candidates who do not meet the required qualifications away from applying for positions or towards more suitable ones. This is only the first step. what are other ways AI is being used by employers in hiring?

Jonathan Slowik: Well, even if an employer can cull down the applicant pool, interviewing candidates is extremely time-consuming and expensive. An employer may only be able interview a small number of candidates. Some employers are now using AI to conduct their initial interviews. This technology is already available. People laugh at me when I say it. The current versions are a bit primitive, and tend to look like chatbots. The interview could take place in a chat box and be a hybrid between text and video. For example, an AI interviewer might ask a text question and then ask the interviewee for a video response. The AI interviewer can then analyze that response, not only for its substance, but for how the candidate presents him or herself by evaluating things like facial expressions and speech patterns.

This is just a personal prediction, but I think this technology is going to get much better in the short term. We are on the cusp of having chatbots who can converse in audio and simultaneously take in visual information in real time. OpenAI posted an interesting demonstration of this on YouTube. It shows an AI conducting a mock interview with a potential candidate. The AI then gives feedback at the end. Among other things, it says, “Try not to touch your face too much.” It can be distracting and might give off signs of nervousness.”

OK, how about this problem, Guy: applicants lie about their qualifications. How can AI assist? Guy Brenner:

Sure. AI solutions can cross-reference social media to verify resumes. Are applicants presenting themselves in the same way as they did to you? Or have they embellished their resumes or overfitted them to your job openings? Video interviewing software can perform a function similar to the one you just described. The virtual interviewer will ask specific questions about the candidate’s experience, and analyze their voice and facial expressions in order to determine the truthfulness of the response. Interviews and resumes can only provide a limited amount of insight into a candidate’s performance in the workplace. Jonathan Slowik:Some developers think so. There are a number of AI-evaluated test on the market which can provide job fit scores for things like cognitive skills and aptitudes. They can also assess personality or “culture fit” and personality traits. One provider’s website features a prompt that is meant to test an applicant’s empathy skills for a position as a customer service specialist. Here’s the prompt. “You are on the phone with a customer who becomes upset or distressed. Record an audio recording of what you’d say to calm down the customer and manage the situation. The AI scores the candidate based on not only the words they used, but also whether or not they delivered them empathetically. Guy, let’s do one more use case. How can AI solve the problem of employee evaluations being time-consuming and subject to recency bias, favouritism and other subjective biases, as well as being subjective? Guy Brenner:

This one is pretty straight forward. A number of products allow AI to prepare the first drafts of performance reviews using objective and subjective sources. It is true that this can save time, but developers claim it also makes the process more accurate. As you might expect, these tools need to be implemented with care. But as one might imagine, these tools need to be implemented with care.

Jonathan Slowik: That’s right, Guy, not least of which because we have existing antidiscrimination laws on the books, and it’s no defense to say “the AI did it.” A number of federal agencies (including the EEOC and Department of Labor) issued a joint statement in April of this year affirming their commitment to enforcing these laws “regardless of whether legal violations occur through traditional means or advanced technologies.” And they identified three types of problems that can occur with the use of AI in particular: first, issues with training data; second, issues relating to model opacity (also called “black box” issues); and third, mismatches between platform design and use. It’s important to understand the limitations and features of these AI systems. Otherwise, employers could unintentionally violate these laws. So over the next three episodes in this series, we’re going to be taking a closer look at each of these categories.

Guy Brenner: Well, thanks, Jonathan. I’m excited to talk to you about these categories and learn more about what employers should know about AI. To those who are listening, thank you for being with us today on The Proskauer Brief. We will record new podcasts as developments warrant to keep you up-to-date on this fascinating and constantly changing area of law and technology. Follow us on Apple Podcasts and Google Podcasts. You can also listen to our podcasts on Spotify.

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