Summary
In this episode of 2030 Vision: AI and the Future of Law, Jen Leonard and Bridget McCormack delve into the evolving role of AI in the legal profession, particularly its impact on gender disparities. The discussion covers personal experiences with generative AI, concepts like tech accelerationism and effective altruism, and how these ideas intersect with the legal field. The episode highlights the urgent need for inclusive conversations and strategies to retain female talent, emphasizing AI’s potential to both disrupt and create opportunities for women in the workforce.
Key Takeaways
- Effective altruism emphasizes maximizing positive global impact.
- Tech accelerationism focuses on rapid technological progress.
- Gender disparities exist in AI awareness and adoption.
- Women exhibit greater caution in adopting new technologies.
- AI could disproportionately affect jobs predominantly held by women.
- Inclusion in AI discussions is essential for law firms.
- Women leaders in law are driving creative AI adoption.
- Clear AI policies are crucial for empowering legal professionals.
- Inclusive conversations will shape the future of law and technology.
Transcript
Jen Leonard: Hi, everyone, and welcome back to 2030 Vision: AI and the Future of Law. I’m your co-host, Jen Leonard, founder of Creative Lawyers, and happy as always to be joined by the fantastic Bridget McCormack, president and CEO of the American Arbitration Association.
On every episode, we try to help the legal profession keep pace with the rapidly changing landscape of generative artificial intelligence, to connect the dots between what is happening in the broader tech landscape and what it might mean for legal. And to do that, in each episode we have divided our conversation into three different segments that we hope are helpful for people.
The first is called AI Aha! Moments. In each episode, we each share something we’ve done using generative AI that we found particularly delightful or interesting, or that helped us understand the capabilities of AI more deeply. Then we segue into some definitions. Understanding AI requires understanding a whole host of new words that we might not be familiar with, so every episode, we define two of those terms and what they mean in the context of artificial intelligence.
And then we dive into a big topic that we focus on for the remainder of the conversation. And I’m really excited, Bridget, to talk about our topic today because I haven’t seen a ton written about it. That might be in part because of the people who tend to be talking and writing about artificial intelligence. So our topic for today is really some emerging research around the disparity in usage and awareness across genders. And also some early research on how AI might impact different jobs—specifically jobs that are predominantly held by women—which is somewhat different from the way that we think about the automation of work and disruption in the marketplace. And so we want to talk about sharing that research and then what it might mean for legal.
AI Aha! Moments: How Generative AI Solves Everyday Mysteries
Jen Leonard: Before we do all of that, though, we’ll start by sharing our AI Aha! Moments—what we’ve been doing on our own with AI. And mine is really, really nerdy this time around, Bridget. And it really suggests that I have a lot of free time on my hands, which it doesn’t feel like I do, but I guess I did during this particular window, because on an earlier episode, we talked about Google’s NotebookLM feature where you can upload files and then click a button and have it convert those files and the content in them into a podcast that sounds very human—just like two people like us talking back and forth about the content.
And it really got me thinking about what it would be like to be an incoming law student right now and have access to some of these tools. In my experience in law school, a lot of it felt very obscure to me. I wasn’t exactly sure what the sort of end game was for me in learning and reading all of this appellate case law. And all of that became really clear in the midst of Socratic Method when I was trying to figure it out in real time in front of all my peers and a learned professor.
So I thought, what would it be like to go back and upload a seminal case that every first-year law student studies and convert it into a podcast? So I downloaded International Shoe v. Washington, which is all about whether a state can assert jurisdiction over a party that’s not within their geographic boundaries. And I don’t actually remember that that’s what the case was; I just knew that we read it and everybody does. So I uploaded it and clicked “create a podcast.” And all I used was the PDF of the case.
So no commentary, nothing extraneous. And it was really, really interesting. It was a 19-minute podcast that came out that I listened to. And it talked all about the underlying facts of the case, what the majority holding was, what the dissenter, Justice Black, thought about the holding and his concerns about sort of the vague definitions that were created around “minimum contacts.”
And then what I thought was really interesting was that without any supplemental information, the podcast hosts sort of talked about why this case would be relevant in a world of internet commerce and a world that’s globally interconnected. And they sort of made the point that — it sounds a little bit cheesy, but is true, and I think relevant for new law students — just because something feels like a dusty old appellate opinion that you’re reading and not fully understanding, these cases echo through time and really have relevance in the world that we live in today.
And to be honest, I never had a professor explain International Shoe to me in that way. And it didn’t make me think that new law students should take these and turn them into podcasts and not read the cases at all — you should read the cases that your professor assigns. But it did make me think that if I had listened to this before or after the case, it would have humanized it a little bit more for me and would have helped me feel a little bit more comfortable with what I was reading and what feels like a foreign language. So that’s what I was doing this week with generative AI.
Bridget McCormack: That’s super interesting, and I had a similar law school experience to you. I didn’t have any lawyers in my family. I went to law school right from college, and I’m not sure I even really understood what appellate cases were when we started reading them. I certainly didn’t understand getting thrown into Civil Procedure, and I was like, “What are we talking about?” Like, I just didn’t understand the context.
So I spent a lot of time trying to figure out the context for a lot of what we were reading, and I bet that tool would have helped me a ton. I would love to know if there are any law professors that are experimenting with it or encouraging students to experiment with it. And if there are and you’re out there, send us an email. I would love to hear from you.
Mine also might make you think that I have more time than I do — and I don’t feel like I do either. But I saw recently Ethan Mollick posted a pretty unbelievable proof of concept for, I think it was just GPT-4, where he uploaded a photo of the interior of a plane he was on and asked GPT-4 to tell him what the photo was of.
And not only did it identify the inside of a plane and where inside the plane, but it was able to identify the particular kind of plane that he was on. And it was so shocking to me, and I had never really done that. So I played around a little bit with uploading photos and having it tell me what it was seeing, and I was stunned over and over again at how accurate it was. It’s funny — it actually slows down and has to take a little bit of time often to “think,” which you don’t see in most of my use cases because I’m not doing anything that’s that complicated, I think.
But I kept trying to get more and more abstract. And there are these recent photos from a NASA mission of Jupiter, and the photos are actually quite beautiful. But I cropped it and cropped it some more and cropped it some more, and it just looks like a modern art photo or like a kaleidoscope image that you might have looked at as a kid. I promise you, if I showed it to you right now, your top 10 guesses would not include Jupiter.
But ChatGPT-4 thought for a couple of minutes. And then it said, “This is a detailed image of Jupiter’s atmosphere showcasing its swirling clouds and turbulent storms. The vibrant colors and intricate patterns are typical of the planet’s dynamic weather system, influenced by strong winds and storms, including the famous Great Red Spot. The image was likely captured by a spacecraft such as NASA’s Juno.”
Which is exactly what it was. I mean, it was literally cropped from a cropped, from a cropped version of it, and then expanded to try and trick the LLM, and I didn’t succeed. And again, not that this is something I have use cases for in my regular life. Sometimes it’s just kind of fun for me to play with what’s magical about the technology still, and I still find it quite magical. And that was a really stunning example of why. So have you played with that feature yet?
Jen Leonard: Yes, first of all, that’s amazing. I also — you know, I’ve heard the guys that we follow on the Artificial Intelligence Show podcast talk about this — that the idea that transformer architecture is guessing the next most likely word in a sequence feels like it makes sense until you do something like you just described with the technology. That does not seem like that type of a process would lead to the output that you’re describing.
I used it for two purposes that you just reminded me of. Just maybe 10 minutes before we started, my adorable little son broke my bathroom scale because he wanted to weigh how much Halloween candy he had earned a couple weeks ago. So I had to order a new scale and I was not paying attention on Amazon and I ordered the same brand I had before, which used to be connected with my phone on an app so you could track data over time, and it wasn’t working.
So I took a picture of the box and uploaded it to ChatGPT and said, “Can you suggest an app that I can connect this with?” And it read it. And then it responded, “This version of this brand of scale is not Bluetooth enabled. It is not connectable to an app,” which makes sense because it was the cheapest scale on Amazon. And now I see why. But it knew right away, it knew the model and it was correct.
And then the second way that I used it: I was in a hotel over the weekend and I could not figure out how to turn the shower head on. Like, no matter what I did, I just saw the spinning thing that you turn — the spigot — to turn on the water.
And so I took a picture in an awkward sort of angle and asked ChatGPT how you turn on the shower head. And there was a little button underneath that I couldn’t see from my vantage point that it saw. And it said, “It looks like there’s a switch under the shower head that you can push to the left to turn it on,” which is exactly what it did. So it’s pretty incredible.
Bridget McCormack: That’s so funny. I have that shower experience. Like, I’m in so many hotels and I feel like I have that shower experience once every couple weeks where I’m like, I don’t understand how I am a grown woman and can’t figure out how to turn this on.
Jen Leonard: Same. And yeah, you used to have to call room service or call the front desk. So at least this early use case is saving me from the embarrassment of conceding that I don’t know how to use a shower.
Bridget McCormack: Totally. It’s the embarrassment that I’m trying to avoid. And so I love you, ChatGPT, for that.
Definitions: Effective Altruism vs. Tech Accelerationism in AI
Bridget McCormack: All right, let’s do some definitions. Today we’re going to define “effective altruists” and “tech accelerationists,” which are not the same thing but are both terms that I feel like I’ve heard a whole lot in the last year and a half or so, and maybe never heard at all before that, even though I think they both have application beyond generative AI. So what do we mean when we say “tech accelerationist”?
Jen Leonard: So my understanding of tech accelerationism — and like you said, some of these words I sort of heard in the ether before — is that it describes divisions among different segments of Silicon Valley and technologists in how they’re thinking about the deployment of technology. One camp are tech accelerationists, who believe in progress in technology at all costs — that that should be the paramount priority of society. The idea being that the advancement of technology is in itself an inherent good and a way to unlock unprecedented human potential. So this isn’t limited to AI; it’s also focused on biotechnology and robotics, with the idea being that these can solve a whole lot of global problems.
Marc Andreessen of Andreessen Horowitz frequently writes from the voice of somebody who is a tech accelerationist. And I know that there have been conversations — for people who are not tech accelerationists — that we are being “speciesist” if we’re not tech accelerationists, which seems like we probably should be thinking about humanity. But for this group, really there’s no limit to how fast we should be developing this tech. The faster, the better. And then we will solve all of the world’s problems.
And the opposite of a tech accelerationist is a “doomer” — somebody who thinks that we should stop developing technology, or at least significantly slow it down, because of the dangers that it could create. But there is another group, Bridget, called “effective altruists.” So could you describe what effective altruism is?
Bridget McCormack: Yeah, so I think effective altruists are motivated by a commitment to figuring out how to do the most good using everything they’ve learned and reasoned through to figure out the best ways to improve the world. I think that’s how they would define themselves. And they’re less concerned with speed and more concerned with making sure that their resources are deployed to produce the most positive impact over other potential priorities, like just getting there fast. And they care about global health and reducing poverty. And they believe that by focusing on those ultimate goods — it’s not just technology; I think there were effective altruists long before generative AI — they can guide the technology to the very best outcomes.
One thing I’ve tried to think about is: if you’re an effective altruist, can you also be a tech accelerationist? Or are you somewhere between a tech accelerationist and a doomer? Do you feel like they’re all plotted along a spectrum, or do they not necessarily overlap at all? Like, are they just different categories?
Jen Leonard: I think it’s a spectrum, and it’s not very clearly defined. It’s almost like artificial general intelligence, right? There’s no clear definition for what makes you a tech accelerationist or an effective altruist or a doomer. We talked about Dario Amodei in an earlier episode — he’s the co-founder of Anthropic, which makes Claude, which we’ve talked about many times. And he wrote an essay about the positive potential for AI, in part because he’s developed a reputation as being a doomer.
And so I think in a single person, you see somebody who is cast as a doomer also trying to be an effective altruist, but also competing with OpenAI and others to develop tech as quickly as possible. So I would imagine in the single mind of somebody in Silicon Valley, in the leadership of these organizations, you might be switching back and forth — depending on the market dynamics and how much pressure is on you — among these different mindsets. What do you think?
Bridget McCormack: I think that’s right. I think probably very few of the leaders in these labs fall into just one of those categories at any one time. I do think there are people who are working full-time on trying to slow down the technology. So I’m not sure that makes them “doomers” and nothing else, you know, if that’s their focus. But my guess is most of the technologists leading these labs have a foot in effective altruism, have a foot in accelerationism, and also probably now and again want to, you know, slow down and take a beat.
So I bet there’s a little bit of everything for people who are doing this full time.
Jen Leonard: Yeah, I brought up Marc Andreessen before because he strikes me as somebody who falls very firmly into that accelerationist territory. And to some degree, Sam Altman — although like you said, he can go back and forth, or he at least acknowledges the dangers of AI but sort of brushes past them or suggests really easy solutions like universal basic income will solve job displacement type things.
Bridget McCormack: Yeah, and I don’t feel like I have enough of a sense of where Satya Nadella or Sundar Pichai — right, the Microsoft CEO or the Google CEO — fall on it. But the actions of those companies feel accelerationist. I would have to know a lot more to figure out where they would put themselves on this spectrum.
Jen Leonard: Yeah, it seems like they’re in such a tight spot, too, because they have to be responsive to their shareholders and be trying to win this race. But I would imagine that lying awake at night, knowing all of the sort of harmful effects of the advancement of this without thought, would also create conflicts, which is sort of the perfect segue into the conversation that we’re going to have today.
Main Topic: New Research Highlights Gender Disparities in AI Adoption in the Legal Industry
Jen Leonard: It’s about gender disparity in AI adoption and some early indications that it might disproportionately impact women at work. And so if you’re thinking about effective altruism and you’re thinking about tech accelerationism — if we’re going to develop this technology as quickly as possible and we start to see these signs early on of these differentials across genders — we really should be thoughtful about how we’re paying attention to that and trying to respond to it in a way that maybe the effective altruists would embrace.
Before we dive in, I would just say that I mentioned at the top I have not heard a lot about this. And like many other topics, I started thinking about it because Ethan Mollick has posted a few times on LinkedIn about some studies around gender disparities. And I think it’s probably because almost without exception, everybody that I follow in this space is a man.
And not that they don’t care about the impact on women, but they might have blind spots around what they’re focusing on in their conversations. So just to sort of lay the foundation for what we’ll talk about — which is why this should matter in the legal industry — there is some early emerging research around gender disparities and two studies that you and I have been looking at over the last week or so.
They come from a July study from the Bank for International Settlements, and then a more recent study that came out in October from the Brookings Institution about the future of AI and the American worker. And the first paper, the BIS paper, really highlights gender disparities in AI adoption. So the paper finds that there’s a generative AI gender gap.
And based on the studies that they’ve done, they find that about 50% of men reported using generative AI in the past year, as compared with only 37% of women. And the upshot of the paper is that they recommend more research around why this is the case. And we can sort of hypothesize why we think it might be the case, but there needs to be more research into causality. But what they have identified early on is that about three-quarters of that disparity can be attributed to a lack of awareness among women as compared to men about generative AI and access to the technology. And the other quarter they attribute to women having a heightened concern about trusting artificial intelligence with their data — so a fear or hesitancy around engaging with frontier models and giving it information.
And that study really complements, in a negative way I think, a second study from the Brookings Institution about how generative AI might actually impact the work that we do and the labor force. And the report makes the point that traditionally when we think about disruption because of technology, we think about blue-collar work, physical labor, and the image that we might have in our mind is of male-dominated professions. But in fact, based on some data that the Brookings Institution studied from OpenAI about exposure rates, first the data generally finds that more than 30% of all workers could see at least 50% of their occupation’s tasks disrupted by generative AI. But there are segments of the workforce whose work will be disrupted more. And they highlight the potential elevated disruption among office and administrative support occupations as having a high automation potential.
And women really comprise the overwhelming majority of the nearly 19 million Americans that are employed in that sector. And they note that this is racially and ethnically a diverse population — there doesn’t seem to be a disparate impact across race and ethnicity — but there really does seem to be a potential disproportionate impact among female workers who are in jobs that traditionally have required a college education and are focused on administrative and clerical work in offices. And the last thing I’ll say, Bridget, before I hear all the things that you think about this, is just that they drilled down in the different occupational sectors and industries around discrepancies in gender in roles. And one that they focus on is the legal industry.
And they note that in the legal industry, among legal secretaries and administrative assistants, 88% of the tasks that they do are more likely to be automated in a world of generative AI. And 96% — that was a number that even surprised me at how high it is — 96% of the people that hold those jobs are women. And then paralegals and legal assistants have about 58% of their tasks that are susceptible to automation, and 83% of the people that hold those roles are women. By comparison, lawyers’ tasks are susceptible in about 32% of the categories of tasks that lawyers perform, but women comprise about 40% of that part of the legal industry. So some really interesting findings here and suspicions about what the future holds, but you also found some different research in another sector.
Bridget McCormack: Yeah, I mean, I’m eager to jump into what all this means or what it might mean for the legal profession. Obviously, I don’t know, but I have thoughts. There’s a BCG study also from 2024. It found that women in tech are using generative AI tools at work even more than their male counterparts, and senior women are ahead of their male counterparts in gen AI adoption. Of course, that’s within the tech industry. And I think it’s still the case that overall in tech, there are more men than women. So I’m not sure where we might make up the delta in technology that we’re losing in legal and other industries. But there’s some findings from that study that I think are consistent with what we see from the other two, which is that women have a lower risk tolerance in adopting new technologies without clear policies and a clear understanding of risks, which can hinder early adoption. That makes a lot of sense given the other findings.
I worry about these findings for legal in particular, because I think each makes me more concerned about the other. Because I’m optimistic about the possibility that the legal profession might be able to all of a sudden solve a lot more problems that right now it can’t. I’m basically an optimist — not a tech accelerationist necessarily, but an optimist — about what it could mean for the legal profession and what that might mean for our communities. But I think unless we’re thoughtful about what it looks like, we could really lose out on a lot of the smartest pathways to that better future. So if we have a gender disparity right now, at the time when probably we all need to be rolling up our sleeves and figuring out what this might look like and how we get to that positive set of outcomes that I think we can get to, we need everybody in that conversation — and we definitely need women in that conversation.
I do think without women in that conversation, we won’t have the best answers for the Brookings study findings. And I think there are good answers there, but they require us to have a fully inclusive conversation about what it all means.
So I feel like they underscore — each of those studies underscores — what worries me most about the other. I’m concerned about them more in tandem than each individually. Do you have other thoughts about why you think it’s turning out this way in so many industries (apart from tech maybe)? I think the risk tolerance thing makes a lot of sense and resonates with me, but you do so much presenting to lawyers and law firms. I wonder if you have any other thoughts about why the disparity in the take-up — which I think will matter for the roadmap that we need to find.
Jen Leonard: Yeah, I don’t actually know why women are less likely to be aware of AI. And I’m always hesitant to sort of sweep with a really broad brush — “women have these personality traits and men have these personality traits.” But like you said, I mean, to the extent that some of the early thought is that women approach things more cautiously and are thoughtful about it, that does make sense to me.
Also this idea — and this is not an all-women thing; I’ll just speak for myself — I’m a rules follower, right? I love innovation, I love coming up with new ideas, but I’m not a Mark Zuckerberg “move fast and break things” type. So if my organization has not communicated a policy to me that allows me to play around with technology, I’m probably unlikely to do it on my own, because I don’t want to do anything to jeopardize the organization’s security.
I think for that reason, it’s so much more important, A) that organizations do what you do at the AAA, which is really encourage people — give people the guidelines for experimentation, but then encourage everybody to be taking part in it. And relatedly, communication is so important. Almost every law firm that I visit has an AI policy in place, but when you ask people, “Are you aware of what the policy allows or what it prohibits?” Most people don’t know, because people are busy and they’re not paying attention to something that sits on an intranet or that went out in an email. So that, for me on a personal level, resonates.
And the other point that you had about optimism: I was also very optimistic when we started talking about generative AI and particularly its impact on the billable hour — that this could be the dawn of a different type of practice in the private sector, where you’re not tethered to the number of hours in your day and your value is not driven by how many of those hours you dedicate to practice, but is really more closely aligned with the quality of the legal work that you deliver. And I don’t think there’s any data in the world that could suggest that women lawyers are not as strong as male lawyers are when it comes to the quality of their legal work. But we know that when women choose to leave the workplace to start families, that there’s a disproportionate burden on their ability to bill hours. And so I was hopeful that this tech could actually help tackle that a little bit.
And so that’s what struck me as — to your point — I am still optimistic for that to be the outcome, but I don’t think it will be the natural outcome if these are some of the early indicators that we’re seeing.
Bridget McCormack: It won’t be the outcome if we’re not intentional, right? But it does feel like — I’ve said a lot of times in the context of legal education — I think there’s some open lanes for law schools that are really intentional and thoughtful about how they use the technology in education to train their students to be competitive in the marketplace. There’s all kinds of, I think, opportunities for an innovator at a law school — law school administrators who are innovators — to really jump the line in terms of how people think about their law school. Might that be true about law firms as well? I mean, could you imagine a law firm saying, “We are all-in on using generative AI tools to support our associates who want to, you know, figure out a different path in how they navigate their relationship with the firm. And we don’t think that if you’re going to be working at home for some period of your employment, that that should impact you. And we now think it doesn’t have to.” I mean, do you see an opportunity for law firms to use an intentional strategy around generative AI to be a place that attracts women? Is that possible?
Jen Leonard: I do. And it feels like a really good transition into the next era of inclusion in law firms, because it feels like it aligns with what we should be doing anyway, which is leveraging technology to provide better service to clients that is more efficient and actually allows them to solve the problems that they came to the firm for. So it doesn’t feel like it’s a forced effort to make something happen that maybe would not be popular. I mean, firms don’t want to lose their great talent, no matter who that talent is. But the current model just creates results where they hemorrhage female talent — really at a time when women are developing clients, becoming really expert in their practice area (if you’re talking about the practice side of the firm), or getting to understand the business and moving into senior roles on the business side of the firm.
So to me, it’s a win-win, right? You want to attract the best talent, regardless of who that talent is, and then you want to keep them there. So it’s also — you know, talking about law schools, having been in a law school for a long time and having started in career strategy, I know how much law firms compete for top talent, and it’s very hard for them to differentiate themselves from one another. And this to me seems like a really creative and innovative way to say, “We take diversity and inclusion seriously, and we go beyond what’s been done before to think about emerging technologies and how women can really build and sustain a career here while becoming experts and serving our clients well.” So yes, I see big opportunities, but it won’t happen by accident.
Bridget McCormack: It won’t happen without women in the design process, right? Designing what your law firm’s approach is going to be has to include women, and they have to therefore be fully read into what the technology can do and how it can support lawyers and how it can make your services better. So it feels like if you’re a law firm leader right now and you’re working on your committee to navigate generative AI transformation, you want to make sure you have a lot of women on that committee. It feels to me like you want women’s voices in the room on this.
I’m also a little bit worried about what it’s going to mean for the future leaders in the profession, not just at law firms but across the profession. I haven’t looked at this data recently, but it’s been consistent for a long time: there are more women graduating from law schools than men, and within public service — and I think the judiciary — women make up a bigger proportion of leaders within those sectors of the legal profession. But they’re still pretty far behind in big law firms and in in-house counsel jobs, at least at top companies. And if this is going to be — and I think it is going to be — an important part of the changing business model, you can’t leave half of your entering class behind in figuring out what the business model is if they’re going to be the future business leaders of the firm, right? So just another reason why you want to involve women at the front end, if five years from now and ten years from now you want them to be in leadership roles in your practice. I don’t know if that’s another thing that you think firms could be thinking about, but it seems to me that’s another way you could differentiate yourself from some of your peers in a competitive marketplace.
Jen Leonard: I completely agree with everything you just said. This could be a way to broaden the pathway to leadership in the private sector, in-house, and in law firms. But as you’re saying this, I’m thinking anecdotally about some of the law firm leaders I’ve had the chance to meet. And it does seem interesting that there’s sort of an over-representation of women chairs and managing partners — as compared with how they’re represented across the profession — who are really eager to think about AI and the future of law firms. So it’s interesting; I don’t know whether it’s because we as women have sort of had a more challenging road in some ways to come up through leadership and are more open to creative solutions or are more just sort of client-centered. I don’t know what it is, but four or five chairs that are women are popping into my head right now as those who I think will really lead their firms into the future, because they have the right mindset around it.
Bridget McCormack: That’s so interesting. This conversation is just like — it’s triggering the same thing for me. I’ve had a couple of interesting conversations recently with women leaders of more, you know, sort of mid-sized or smaller to mid-sized law firms who see this technology as an opportunity for their law firm to do new things that maybe it couldn’t do before, given its size and reach. And they have this creative mindset that I do think is part of the winning formula for navigating the opportunities and the risks that that technology presents. So this will be really interesting to watch. I wonder if there’s any way for us to collect any data about it or check back in on it after some amount of time. It would be a cool study to do along this path across law practices. I’m interested.
Jen Leonard: Yeah, absolutely. It’ll be interesting to follow this. And like you, I’m now thinking of women judges that I’ve talked to who are sort of disproportionately saying to me (as compared with male judges), “I want my clerks to learn how to use this. I want to generate opinions more quickly.” It’s not suggesting there aren’t men saying those things; it’s just interesting now that I think about it who is coming to the front. I think I mentioned — I don’t know if on the podcast or just to you — that I did a presentation once and a woman told me that she was about to turn 80 years old and is a lawyer and a therapist who works in family law, and she is all about this because she sees an opportunity to scale education to people who have really limited resources to serve their clients. Again, I’d hate to overgeneralize, but part of this might be the idea of community and being interested in the scalability of using these resources to serve more people. So super interesting.
Bridget McCormack: I think this is one we can probably come back to. And I think our friend Kat Moon at Vanderbilt Law School is at least planning a gathering about this topic — about gender and AI. I don’t know if you have more details on that. We can return to it later if we don’t.
Jen Leonard: Yeah, I love Kat and everything that she does. I follow her on LinkedIn. I’m not on any other social media, but I think she’s on every social media platform. But as we were prepping for this episode, I just happened to see in my feed that Kat was advertising a conference on AI and women at Vanderbilt Law in February, I believe. So if you’re interested in learning more, I would find Kat on LinkedIn and just click through her recent posts, because I’m sure it’s up there — or Google, you know, “Vanderbilt Law AI and gender,” and you’ll find Kat there.
I also think, to the earlier discussion of firms and schools, the good news here is that there are some structural components that can support conversations that are really different and new in all of these places. Every law firm has a women’s committee in it, and they are always struggling to try to figure out how to advance women leaders in the firm.
And every law school in the country has a Women’s Law Association or some sort of group that is focused on gender equity. And so I hope that maybe conversations like this actually raise awareness that there are new topics and new opportunities for innovation there.
Bridget McCormack: It’s a great idea. Most state bars that I’m familiar with have a Women Lawyers Association — you know, Women Lawyers Association of Michigan — and then there’s county-level versions of it. And I know they do lots of interesting programming, but what a great idea, right? If you’re the incoming chair of some women’s lawyers bar group to say, “We’re going to bring in speakers and we’re all going to start sharing use cases.” And what a great opportunity to make this a focus of that kind of organization. I love that idea.
Jen Leonard: And if we can get a whole bunch of smart women focused on this, I think we have unlimited potential. So thank you so much, Bridget. I hadn’t even really thought through a lot of the things that we ended up talking about today. So I’m grateful to you.
Bridget McCormack: That’s why these conversations are so fun. Same with me.
Jen Leonard: Well, thank you, and thank you to everybody for listening. We’ll look forward to seeing you and sharing what we’ve learned next time on the next episode of 2030 Vision: AI and the Future of Law. Until then, take care and be well.