Aug 18, 2026 · 21 min · 9 segments
Measuring what matters with AI-driven monitoring and processing tools is becoming fertile ground for legal risks in the workplace. In this *We Get AI* episode, co-hosts Eric Felsberg and Joe…
Eric FelsbergHost
Joe LazzarottiHost
I know that we want to touch on a topic today that I think is pretty interesting in terms of technology from a whole range of different areas, but it really all centers around monitoring employees and their productivity.
So I know there's been some recent AI laws that kind of deal with that a little bit, but Maybe just to introduce the topic more thoroughly, there are some platforms that allow employers to kind of get a better, sometimes too much of a window into what their employees are doing.
And then the analysis of that data sometimes is processed by what we're all calling is AI.
But the end result of that or the purpose of that is to kind of get a sense of how productive the workforce is.
And there's a lot of questions that go into that, like what to measure, how to measure it.
In a lot of cases, are we going too far in measuring it? So I think that's kind of what we were thinking about addressing today.
You know, there are a lot of new technologies that are coming on the scene and it feels like we hear about a new one each day and we probably do for that matter.
But some of the technologies, to your point, that that we've been hearing a lot about lately are these monitoring tools.
Usually they come to us and, you know, an employer will say, look, we want to use this tool not for personnel employment decision making.
And then you and I will have a discussion with them to try to figure out exactly what it is that that tool does.
And at least from my perspective, first I want to understand what the tool does, but immediately what I'm thinking about as it's being described to us is, well, which laws out there potentially address this or are implicated by its use? And, you know, you and I were chatting offline about, you know, if you think about the New York City AEDT law, which deals with automated employment decision tools for jobs in the city of New York, that applies to hires, which I think a lot of, you know, in terms of the AI dialogue that's been kind of emerging over the last couple of years, I think a lot of people are very familiar with the use of AI in the hiring context, but I think what a lot of people don't spend much time thinking about is it oftentimes goes beyond that.
And in New York City, just using this as an example, it also applies to promotion, promotional decisions.
Again, it's being used to streamline our processes, make improvements to our workflow or whatever the reason is for its use.
The question that always comes to mind is, well, what is that output that that AI tool is producing? Is it scoring candidates, for example? Is it somehow categorizing more productive employees versus maybe less productive employees? And from there...
Even though kind of the stated purpose is to look at productivity type issues and not employment decision tools, it kind of begs the question if later on I want to use some of the output from these AI tools to make promotional decisions.
am I now under the New York City bias audit requirement? And let's say that I am, how do I do a bias audit? I think we understand how to do it with hiring, where you take all the applicants that were assessed by the tool, and then of those applicants that were assessed, which ones received a favorable outcome, meaning a quote-unquote passing score, so they're moving forward in the hiring process, and which aren't.
It's the same thing in promotions, except you're dealing with a much smaller, usually I shouldn't say always, but usually much smaller data set because it's all of the employees that were assessed or reviewed, again, arguably by these monitoring tools.
I know that we want to touch on a topic today that I think is pretty interesting in terms of technology from a whole range of different areas, but it really all centers around monitoring employees and their productivity.
So I know there's been some recent AI laws that kind of deal with that a little bit, but Maybe just to introduce the topic more thoroughly, there are some platforms that allow employers to kind of get a better, sometimes too much of a window into what their employees are doing.
And then the analysis of that data sometimes is processed by what we're all calling is AI.
But the end result of that or the purpose of that is to kind of get a sense of how productive the workforce is.
And there's a lot of questions that go into that, like what to measure, how to measure it.
In a lot of cases, are we going too far in measuring it? So I think that's kind of what we were thinking about addressing today.
You know, there are a lot of new technologies that are coming on the scene and it feels like we hear about a new one each day and we probably do for that matter.
But some of the technologies, to your point, that that we've been hearing a lot about lately are these monitoring tools.
Usually they come to us and, you know, an employer will say, look, we want to use this tool not for personnel employment decision making.
And then you and I will have a discussion with them to try to figure out exactly what it is that that tool does.
And at least from my perspective, first I want to understand what the tool does, but immediately what I'm thinking about as it's being described to us is, well, which laws out there potentially address this or are implicated by its use? And, you know, you and I were chatting offline about, you know, if you think about the New York City AEDT law, which deals with automated employment decision tools for jobs in the city of New York, that applies to hires, which I think a lot of, you know, in terms of the AI dialogue that's been kind of emerging over the last couple of years, I think a lot of people are very familiar with the use of AI in the hiring context, but I think what a lot of people don't spend much time thinking about is it oftentimes goes beyond that.
And in New York City, just using this as an example, it also applies to promotion, promotional decisions.
Again, it's being used to streamline our processes, make improvements to our workflow or whatever the reason is for its use.
The question that always comes to mind is, well, what is that output that that AI tool is producing? Is it scoring candidates, for example? Is it somehow categorizing more productive employees versus maybe less productive employees? And from there...
Even though kind of the stated purpose is to look at productivity type issues and not employment decision tools, it kind of begs the question if later on I want to use some of the output from these AI tools to make promotional decisions.
am I now under the New York City bias audit requirement? And let's say that I am, how do I do a bias audit? I think we understand how to do it with hiring, where you take all the applicants that were assessed by the tool, and then of those applicants that were assessed, which ones received a favorable outcome, meaning a quote-unquote passing score, so they're moving forward in the hiring process, and which aren't.
It's the same thing in promotions, except you're dealing with a much smaller, usually I shouldn't say always, but usually much smaller data set because it's all of the employees that were assessed or reviewed, again, arguably by these monitoring tools.
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