EP 165: Ethical Leadership for AI Implementation in the Workplace

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What happens when a company does implement artificial intelligence into the workplace? Right. We always talk a lot about what happens on the front end, but what about when you start to see vast improvements and efficiencies and roles completely changing? What happens then? What is the ethical thing to do when you are implementing AI in the workplace? We're going to be talking about that today and more on Everyday AI. Welcome. I'm your host. My name is Jordan Wilson. And Everyday AI is for you. It's for all of us. It's so we can all learn what's going on in the world of artificial intelligence and how we can use it to grow our company and to grow our careers. And hey, our guest today is perfect for that. Talking to an AI product manager from Indeed. So what, what better way to talk about how it impacts both our roles and our companies? But before we get into that, thank you for joining us. If you are joining us live, get your questions in. What do you want to know about ethical implementation into of of artificial intelligence into the workplace? If you're joining us on the podcast, thank you. As well, make sure to check your show notes. We always have other resources linked there. You can even drop us an email follow, follow me on LinkedIn and connect there and join a future live stream. But before we get into that, let's go over, as we do every day, what's going on in the world of AI news. So politicians are looking to AI for their next job. Speaking of jobs. Right. So former speaker of The House Kevin McCarthy is exploring a new a new career in the worlds of space and artificial intelligence. So according to an Axios report, he's using his relationships with figures like Elon Musk to bridge the gap between technology and government. So he sees AI as a positive force and wants to keep California and D.C. to better understand each other, especially when it comes to artificial intelligence. So interesting. Kind of the first big name politician that we've heard that is looking to transition into the AI space. Next piece of news, Google. Yeah, sorry, Google, after roasting you the other day on Gemini. Hey, some some more positive news, but Google Duet is upgrading its AI assistant. So Google has unveiled upgrades to its AI enabled assistant, Duet AI for Google Workspace. So the major development here is bringing Gemini to Google Duet, which should launch in early 2024 and improve the tool's capabilities in understanding and generating texts. As well as providing those multimodal capabilities. So that's the big difference between, you know, Gemini versus its previous model, Palm 2 is Gemini is built multimodal at its, at its core versus, you know, stacking those multimodal capabilities on top. And then Google also announced the general availability of Duet AI for developers and Duet AI in security operations. So, yeah, I'd love to hear even from our audience, what are your thoughts on GOOG Google, Google's Duet, do you think it's going to compete with Microsoft Copilot? I'd say probably not, but time will tell here pretty shortly. Last but not least, OpenAI strikes a deal with news publishers. So OpenAI is the owner obviously of the popular chat chatbot Chat GPT and they've struck a deal with European news publishers Axel Springer SE to bring their content to train their AI systems. So this partnership marks OpenAI's second agreement with a news company and aims to explore the potential of AI powered journalism, which we obviously talked about on the show yesterday, with the New York Times bringing on ahead, essentially ahead of AI and creating an AI department in their newsroom. So this agreement here is worth tens of millions of dollars and will reportedly last for three years. All right, that's not all. There's always more news. So make sure if you haven't already, go to your everydayai.com sign up for that free daily newsletter. Hey, Even speaking of AI, sorry, speaking of OpenAI, there's the, the registrations are back up for ChatGPT plus mid journey is now available on its own website outside of Discord. So we're going to have all of those things, all of these news pieces and a lot more. But that's not what today's about. Today is about talking about ethical and keyword on ethical, right, Ethical leadership for AI implementation in the workplace. And I'm extremely excited for today's guest so we can talk about how do we do this ethically. Right. When we bring artificial intelligence into a workplace, things change, people's roles change, their jobs change and it's a lot to, it's a lot to navigate. So I'm happy to bring on an expert to help us navigate that. So please help me. Welcome to the show. Bring her on there. There we go. We have Madison Mods, who is the AI product manager at Indeed Madison. Thank you so much for joining the show.

Thank you so much for having me Excited to be here and welcome everyone who's watching today. Really excited to hear your thoughts.

All right, and hey, just real quick, Matt, Madison just What is, what does that even mean? What does an AI product manager at indeed do?

That is a great question. Everyone's out here using AI as a buzzword, so yeah, I'd love to put some context to that. So product management overall as a discipline at many companies is really focused on bringing forward a strategic vision for a specific area of most of the time a digital product. And so product managers can function in a variety of roles, they can work on product growth, they can focus on more technical components, they can focus on user facing applications. And so there's a lot of different subsectors, I would say, of what product management entails in terms of what does your day to day look like? It definitely changes all the time. So specifically, AI product management is looking at how can we leverage different types of artificial intelligence tools in order to optimize optimized for experiences for users, for our site or internally for our own company to move and progress forward in the most high velocity, most efficient and hopefully the most broad reaching way to downstream impact all of the people that are using our products. So yeah, you can kind of think of that role as, you know, looking internally and figuring out what is the best path forward. But also there are a lot of AI product managers that focus on kind of this more external facing implementation as well.

So, okay, so now we know a little bit about what an AI product manager at Indeed does. But I think actually it's interesting to talk and to go back and forth a little bit on what Indeed is actually doing in the space. Right. Because when we talk about shaping the future of work, a lot of people don't know it might start at that place where you're applying for the job or, you know, applying for your next position. So Madison, can you talk a little broadly just about how a large company like Indeed is even help specifically using AI to create better connections between job seekers and employers and also how they may even be impacting the future of work by making those hopefully better and more personalized connections.

Totally. Yeah. HR tech and AI is a huge hot topic. I'm sure with the AI act coming out of the eu, a lot of people are thinking about this top of mind as one of the categories that is in high risk. And many, many companies, including Indeed, have been using AI in their products for years. And one of the reasons is that being an HR person, either formally or informally at your company is very hard when you're sourcing candidates, you're trying to find the right person in this pool of millions and millions of people and especially for small businesses, you might not have the time to kind of look through all of those possible candidates that are coming in, reach out to them and so on and so forth. So indeed is really looking at bridging those connections between employers, regardless of how much time they have to contribute to the HR discipline and candidates that might be a good fit for their next role. So we use a lot of machine learning techniques in order to bridge that gap between those two sides of the marketplace. And so operationally, like all of the recommendations that you see on our website, how those recommendations are ordered, ordered and so on and so forth, are all powered by different machine learning algorithms that intake tons and tons of data that we have about our users, whether those be our, you know, employers and the jobs that they're posting, as well as the job seekers that are perusing our site and, you know, demonstrating their preferences for what they're looking for in their next best job.

And you know, one thing, and we didn't even talk about this beforehand, Madison, but you bring that up and I'm curious because I do remember, you know, we cover the AI news every day and you know, one thing that we've, we, we've seen and we've covered before is, you know, all these different reports and that are just saying, hey, you know, Gen AI is, is coming everywhere. So, you know, even indeed's AI at work report, you know, showing up here on the screen, if you're listening on the podcast, you know, says finding that Gen AI will impact almost every job in America. You know, can you, you know, just talk briefly about how, you know, even just from your personal opinion, how do you think generative AI is going to change the future of work? Because I've said it on the show here many times, I said, if you're not already prompting hourly, you probably will be soon. And you know, whether that's in, you know, two months or two years. But how do you see just the average job, you know, changing in the future?

Yeah, that is a really, really great question. There's a, there's a lot of research out here about like the displacement of work and I'm happy to get into that more. Many, many tasks are going to be aided by generative AI systems. I think that the key difference between, and someone put this in the chat as well, the distinct, the distinction between predictive and analytical AI versus generative AI is that what we're doing here is we are, we're generating net new content, right? It's obviously trained off of a large corpus of data that is historical in nature and is pulling from human insights from across many, many different disciplines. But the thing that really great about this is that it shrouds or it gives the illusion of like human creativity. It's coming up with things that are, that can be mimicked, I guess, as what we, we can typically experience in our roles at work. And I know personally in my, in my workplace there are tons and tons of folks being encouraged to use generative AI in their, in their work. I think for me as a product manager, one of the main things I'm responsible is for, is for driving kind of our product vision and strategy forward. And sometimes it's hard to kind of put words to those things. So I found myself kind of brain dumping into ChatGPT like, hey, here's all the things that are in my head at any given time. I'm working on 10 different projects at once. I'm trying to understand like, what is that connection between all of these things. And then once I can kind of like pretty much talk through things with not a real person, but someone that can help me synthesize that information, it can help me, you know, basically make sense of a lot of jargon and like random stuff that is coming out of my head and put it into something that is really easy for all the teams that I'm trying to mobilize forward to this strategic vision and giving, giving some words to the things that I've been driving kind of in the day to day, but hopefully kind of projecting a more future oriented outlook onto the work that I'm doing. Yeah.

With my team and I, I think Madison, you know, as, as we, you know, kind of get back to a little bit more the actual topic of today's show. Right. I think when people think AI implementation, they think gains. Right. They think efficiency, they think the company's, you know, balance sheet, you know, they think that AI is just, it's, it's going to be a win, win for everyone involved. But that might not always be the case. Right. So what are maybe some things that we need to look about, look at, especially when it comes to ethical implementation of AI in the workplace, that maybe things that may not be wins for everyone.

Totally. Yeah. Let me give you a little bit of context of why I wanted to talk about this today, because this is a, I think as an AI optimist with a little bit of, you know, cautious optimist I guess is what I would call myself. I didn't really realize when I came into my role that I'm in today. AI was like this exciting new thing. I kind of framed it in the same way that you just framed it as well as many other business leaders framed it. It's a way to make things more efficient and better all around. And when I was brought onto my team, my main role was to take a primarily manual operations team and transform their existing workflows to be augmented by AI. And so really this was a large strategic focus for our company at the time because we were trying to expand our international influence. And in the specific discipline that I was working in, which is called taxonomy, we needed to be able to scale very, very rapidly with as little cost as possible. So for each international market, let's say historically, we would be able to have like 8 to 10 operations analysts kind of spinning up a market. We wanted to experiment with maybe one or two people instead of just like large team that we had built out and see kind of what, what kind of gains we could get with implementing machine learning. And I was like, that seems like a freaking cool project. I'm really excited to work on this. This was one of like, like my first forays into implementing AI. And when I went and pitched this to my team, they were like, cool, but like, what about us? And I didn't really. I think because of how my specific role was incentivized, like mainly my role as a product manager is to make things faster and increase velocity and make things better overall. But I think in terms of making things better, I wasn't really considering what was going to happen to the employees that were training these algorithms that had the potential to eventually displace their once super multi skilled roles. These are people that are coming out of PhD programs and are, you know, linguists and information architects that have all of this education and training. And I'm reducing them to a labeling task. And I think that is one of the hidden. The hidden things behind implementing AI is like these models are extremely powerful, but in order for them to be performant, they need to be checked by an expert. And that expert is in charge of fine tuning those systems. And in order to do that, they have to dedicate and change a lot of their role to basically train a machine to do their job well. And if we're not intentionally taking time to help them envision the future of their role, it can be a very scary and daunting task for those folks. If there's nothing in it for them, why would they want to go and implement these things? That was one of the major things I think on my team when we were incorporating machine learning is treating it as more of an augmentation device instead of envisioning it as a replacement. We were trying to figure out, like, what are the things that in an ideal day for these analysts, if you had unlimited time, what would you want to spend your time on? And what things can we actually outsource to a machine learning algorithm that, you know, maybe are core parts of your job, but aren't things that you actually enjoy doing or feel like you are your own specialty and expertise? And so that's one of the things that I think really what's revealing to me about, like, what is. What are the AI Implementation is always seen as good and creating gains, but we have to ask the underlying question, like, who wins and who loses in these situations.

Yeah, and that's such a good point, and I'm excited to dig down deeper. And if you are just joining us now, make sure to get your questions. And now as a reminder, we have Madison Mons, the AI product manager from Indeed, joining us. Madison, you brought up. It's the elephant in the room that no one wants to talk about, but I'm always fine talking about it. You know, my thoughts are, you know, implementation of AI, especially across enterprise, will lead to a lot of job displacement. People don't want to talk about it, but I've always said, yes, AI will take a lot of jobs, but ethically, like, ethics is. Is the key thing there, right? So it's like, what will companies do when they find out? Yes, oh, you know, velocity. Yes. This AI makes us faster. It makes us more efficient. Now all of a sudden, you know, instead of people feeling like they have way too much manual work, it might feel like, oh, maybe they don't have enough on their plate, or maybe their jobs are kind of like what you said, Madison, maybe a little more. More menial, you know, tagging things, you know, for, for models. When it comes to the ethics, how should companies be looking at this? Right, Because a lot of companies care about their shares, right? Like their shareholders, especially if they're public, they care about profits over people sometimes. How should companies be looking at this? You know, because if they do AI implementation correctly, in theory, it is going to completely change how their business operates in a very quick period. How can leaders go about tackling something, you know, that huge in that. That carries that much weight? How can they do that?

Yeah, I think it really boils down to two things, which are consent and attribution. And I think the first step here is if you are going to be implementing AI in the workforce tell it like it is, you know, like, we can anticipate that there are going to be challenges. And I think getting people exciting, excited, and moving them forward and mobilizing them is a great thing, but also making them aware of what actually is going to happen if this thing works. Right, and having a plan and a path forward. I think it's really important that employees that are going to be impacted in any way, shape or form, whether that's just a task or their entire role, to understand, like, and be able to visualize from their leadership, rather than having to take that responsibility on themselves, to feel like their leadership is supporting them and their future career path. So I think that's number one, is really just being upfront about what this whole thing actually means at that individual contributor, contributor level and what's actually expected of them. And I think the other thing is attribution. Like, who actually gets to claim these successes? I think oftentimes in companies, it's people like me who are rewarded and are kind of like the face of this project that get to take all the credit for these huge gains. I get to put on my performance review that I've saved our company over 12,000 working hours by implementing some sort of automated workflow. And, like, everyone's like, yeah, Madison, you're amazing. But who actually was the ones who were able to actually train that algorithm to be performant enough to actually save those hours? Right. Like, I think a really good example of this outside of my company. And if you guys are interested in this, there's a ton of literature on this called. It's kind of framed around this term called ghost work. So you can think about, like TikTok, for instance. It's one of the most impressive personalization algorithms that exist today. We don't have to see any of the really brutal or, you know, censored type of content on TikTok, because there is a slew of labelers that are, like, outsourced to these third world countries that are keeping your feeds safe. And so you have to think about the emotional labor of these labelers that are having to watch this extremely traumatizing content in order to keep your special, funny TikTok page of cats and cool grandma videos, you know, safe from having to watch that content yourself. So I think being able to incentivize and recognize the people that are behind these systems, but monetarily, because outside of just being like, hurrah, you guys are great at your jobs. You know, these companies are making tons and tons and tons of money from these gains and efficiencies. And I do think because there's this very extremely detailed supply chain in which this data gets refined and fine tuned in order to reap these wins and successes, the profits from those types of huge successes in companies need to actually trickle down to where they originate in the first place. So yeah, it's kind of a mix of, hey, let's get everyone to consent into this, make it very clear what their path forward is and making sure we're upskilling and reskilling them if their roles are going to be displaced or impacted in any way, and then two, just really making sure that we're recognizing this, this hugely interconnected group of people that are really bringing these things to the forefront through actual attribution and recognition as well as monetary compensation.

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Yeah, that's a really great question. And I think this is a challenge that a lot of folks like myself that are working on internal optimization face especially when you're working with very small groups of people. So like in my team, for instance, I've got a team of about 60 to 70 analysts. So even if I am trying to measure efficiency, you're not going to get any stat sig types of metrics that are going to show like, hey, when we implemented this tooling or this new processor, this new workflow, efficiency gains happened, you know, across the board by this percentage. Right. I think it becomes then tempting to look at individual performance and saying like, okay, before we implemented this specific AI technique, you were producing this amount per day and now you're producing this amount per day. But I think that really gets besides the point because we then get into kind of this like micromanagement type of mode. And so I think in terms of like, what metrics we need to be changing when measuring employee performance, I think we need to be looking a little bit more downstream. So rather than looking at like, what are my specific employees benefiting from this system, what are the downstream impacts? So one of the things that I work on my team as the taxonomy team is we're really focused on building out the internal structured data library that describes the world of work. So we've got an outline of all of the different skills that are available, different benefits that companies offer, maybe different work settings that are available, different licensure, so on and so forth. And I could look at my team and be like, okay, how many new skill attributes did you produce this month? Right. But really what I should be looking at is the performance and the incremental gains and where those pieces of data are actually being used downstream. So the addition of the 6,000 new skills that we identified this month was able to increase our ability to drive connections between employees and job seekers by x percent. And so I always encourage people to think about business metrics internally. Obviously it's nice and it's nice to be able to report out those gains, but in small teams, it's more important that you're empowering people to think about the downstream product impact of the work that they're actually driving, rather than like, what are the like one to two types of things that I can slide in there just to meet my quota. So that's how I personally think about it. I like to try to like defer things as far down the funnel as possible so that I can make sure that my, my team is actually thinking not about what is happening at the top of funnel and doesn't care about how that actually Trickles down. Really encouraging that kind of end to end thinking.

That's great there. And if you all missed that one, I mean, talking about how AI gains impact people in the end. Right. I think that's super important. Another, another question here from Ben. Ben, thanks for joining us as always. So saying humans already struggle to be ethical, right? Can AI help? Yeah, can AI help to implement AI into workplaces? Madison, what's your take?

Yeah, I do have some hot takes on this. Is it true? Here we go.

We got to them.

There's a term called techno solutionism, which is basically saying that technology can solve society's largest problems. I think HR tech is a perfect example of this. So we've known since the day of the dinosaurs that when humans are hiring people, there are inherent biases. When you're looking at a person, you're taking in things about their appearance, about how they behave. You can make inferences about where they went to school. All of these things are things that humans naturally are assessing when they meet someone in real life. Some of those are conscious biases, some of those are unconscious biases. And there's been a lot of AI products that have been released that claim to be able to take out human biases and scrub datasets of what we would call personally identifying and information in order to make the most objective hiring decisions. Now, I don't think that that actually fully works because there are a lot of implicit things that a computer can pick up on that, you know, humans would be able to perpetuate those biases. And machines are also perpetuating the same biases. Really good example of that. If you haven't already read about this, this is a couple years back, but there was a machine learning algorithm that was employed at Amazon to hire more engineers. And their training data set was a bunch of really successful resumes of people that already worked at Amazon. And those resumes were primarily from male engineers. And so the algorithm, when it was trying to find net new candidates based off of that algorithm started to just kick out all of the women. And it didn't even need to be like they, they had taken out all of the names, anything that could be maybe indicative of like being a female. But at the end of the day it was still able to pick up on things like if someone went to a woman's college, it would kick them out and so on and so forth. So I think it's really important to notice that yes, humans are inherently have problems with biases and we can infuse those either directly and or machines can kind of pick up on those unconscious biases from patterns and data that we might not even realize. So I definitely think that there are promises that AI can make more objective decisions, but that really requires that our underlying data is not biased. And we know about concepts like systemic racism and, you know, racial capitalism. A lot of the documentation that we have in these historical training data sets are reflective of human biases, and it's very hard to take those things out. So I do think that we'll never really want to fully automate some of these higher risk types of activities, especially because we don't want to, you know, unintentionally perpetuate those. And we should always have some sort of human expert in the loop, hopefully one that is, you know, as free of bias as possible, but really trying to make sure that we have this expert validation that what a model is presenting is not going to be harmful to specific groups of people.

Cecilia with the comment here, just hit it on the head saying machines learn their biases from the humans that program them. Absolutely. I think we have time for one more question from our audience here, Madison. So great one here from Nadia. Nadia, thanks for, for joining us. So saying how do you train employees to use AI ethically and how does implementation go across different generations within the company? That's great because, you know, business leaders I talk to, especially from smaller or more medium sized organizations, are sometimes hesitant because they're like, okay, well what if employees just use AI to do as little work as possible and what if I can't measure it? Or what if we can't implement it, you know, correctly or ethically? So what's your take on that and how you can actually train employees to use it ethically?

Yeah, that's a really great question. And I love the generational take here. I am a Gen Z. Whether I'm proud of that or not is debatable. I'm like cuss between Millennial and Gen Z. And so I am one of the folks that grew up with technology. I was not an iPad baby, so shout out to me for that. But I grew up with the Internet and so I'm used to things and things that think for certain generations were things that were adopted later in their lives. It can be very scary. And I've definitely seen that impact in my own team of people that are familiar with technology. And I think there's a lot of like, obscurity that happens with technology. Like, people want to make it sound cool and fancy. Machine learning is really not that it's not it is a black box to a certain extent, but it is controllable also. And I think being having more education around what machine learning and other types of AI techniques actually do and what they're trained off of, what they can do, what they can't do, having that transparency can make things a lot more exciting, a lot less scary. And I think that in terms of training employees to use AI in their roles, ethically, you really need to be thinking about what like, is having someone use ChatGPT to respond to emails a bad thing? Like that's debatable, right? Like maybe if you're a client facing person, maybe that isn't something we want to do because we are putting our genuine thought and time into the clients that we're working with. But if this is my 15th email of the day of something that probably I could have solved in a completely different way, maybe I want to make those tasks easier for myself. And now I have more time to do the task that I'm really actually assigned to do, which is not responding to emails. So I think you really need to be thinking about like, what are your core tasks, what are your core responsibilities? Finding ways to lessen the distractions of the other types of things that happen at work so you can focus on those things that you're really good at and that you're, that your employees really want to be working on and then finding ways to augment those activities with AI when, when those activities aren't necessarily beneficial to the business. So that's kind of how I would think about it. I do think that like everyone's going to have their own opinions on like what is ethical use and what is not and having guidelines around that is helpful for sharing. But I do think that it is, it's a tough, a tough line to cross for sure.

Madison Today's episode we've covered way more than I thought we could possibly cover in, in 30 minutes. But you know what would as we kind of wrap this episode up, put the holiday bow on it, what's kind of your one biggest takeaway, you know, for business leaders out there when it comes to especially ethical leadership and implementing AI in the workplace? What's the one takeaway? What's the one thing that is non negotiable that they, that they need to do in order to ethically implement AI?

Yep. I think a lot of times mandates for AI implementation happen in senior leadership spaces as well as from boards. A lot of those people don't engage with the folks that are actually doing the work. So I'd encourage you, if you are one of those leaders, go talk to the employees that are going to be impacted by the implementation of AI. By no means do you need to stop it. You need to be conscientious of the humans that are involved in the process of AI implementation and the impact that it has to their, their careers and their livelihoods. Obviously, people that have the type of influence to be able to spread these innovative ideas across the company already in positions of power. And so we want to make sure that we are allocating time and attention to the folks that are going to be doing that work, understanding their concerns and their fears, and carving out intentional space for them to have a place in your company's future, whether that be in their existing role or whether that mean they need to kind of do what it takes to kind of improve the velocity of the area that they're in and then use their skill sets in a different capacity. So that would be my main thing. Just keep the humans in the process. There's, there's tons of research on having experts in the loop, and we need to also care about what happens to those experts when their expertise can be largely mimicked by the technologies that are evolving so rapidly.

You know, we always talk and think and strategize around ethical implementation. And I think Madison, what, what you just were able to walk us today and talk us through was fantastic. So thank you so very much for coming on the Everyday AI show and telling us and showing us the path on ethical AI implementation. Thank you so much for joining us.

Thank you.

All right. And hey, as a reminder, this was a lot. If you're anything like me, I have more notes than I can even look at right now. I'm literally typing live as Madison is dishing out all of her expertise, expertise on the topic. So if you haven't already, make sure to go to your everyday AI.com sign up for that free daily newsletter. We're going to be putting all those notes. Yes, me as a human, I'm going to get off this call. I'm going to type up a newsletter so you can read and you can learn. We're going to share other resources as well as other news pieces and what's going on around the web in the world of artificial intelligence. So thank you so much for joining us and we hope to see you back tomorrow and every day for more Everyday AI. Thanks y'. All.

And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode Please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.