What Good Policing Should Look Like with Mo McGough

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I'm Jeff Asher, and this is the Jeffalytics Podcast.
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12%.
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That's how many sworn law enforcement officers in America are women, a number that hasn't meaningfully changed in decades.
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Mo Magaw has spent her entire career trying to understand why that gap persists and what can be done about it.
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A former DOJ attorney and self-described cops kid, she helped build a program at NIJ called Leeds, which identifies and develops law enforcement officers committed to evidence-based practices.
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She also co-founded 30 by 30, a national movement to improve the representation and experiences of women in policing, which is now active in over 400 agencies across the country.
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In this episode, we get into what it actually takes to move a profession that's always slow to change, and the enormous untapped opportunities out there in better understanding how police departments hire and promote.
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Few people that I've talked to have moved policing along as a profession more than Mo, and our insights into what the future policing might look like are invaluable.
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Let's get into it.
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My guest today is one of my favorite people in the policing data crime criminal justice space, Mo McGaugh.
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Mo, thank you so much for joining me.
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Thanks so much for having me.
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Good to see you, Jeff.
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So, first off, can you walk me through your background?
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Because it's like a, I don't know, it almost feels like a forced gump like background of just like having done all of this stuff.
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I don't know.
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That's maybe overselling it.
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I may have I've set a high bar now so far for the rest of the interview, but walk me through that.
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No, I think that's fair, Jeff.
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And I I hate when I'm like interviewing propositions or things like that.
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And people like, tell me a little bit about yourself.
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Yeah, my resume doesn't really have a through line, but I'm an attorney by trade.
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I spent most of my career at the U.S.
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Department of Justice.
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Really fortunate to work on a broad range of issues, everything from transnational organized crime to preventing human trafficking in the US to working on counterpoaching issues in East Africa and AIDS relief in Rwanda.
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And it was while I was at the US DOJ, though, that I was really exposed to a lot of the sort of persistent and pervasive issues that are confronting our criminal justice system here domestically.
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And the more I was exposed to those issues, the more it really felt like the US DOJ was optimally positioned to affect positive change.
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So I'd say about maybe 12 years ago, I really started focusing in on systems level reform and then more concretely on issues affecting policing and public safety and jurisdictions across the country.
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Had been really involved in the evidence-based policing movement, done a lot of work around recruitment retention with a specific focus on improving the representation of women in peace and security systems.
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And then most recently, really focusing down and in on advancing civil rights in the public safety context.
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So you said you don't have a through line, but my next question, I say the one through line in your career is it's all about improving policing and doing it in sort of in a smart way.
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Why is that so important to you?
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And sort of what is your ultimate goal with your work?
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Do you have one?
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Jeff, I don't think anybody's ever asked me that question, and I really like it.
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So I think there's a couple of reasons why it's so important to me, and they they definitely directly relate to my ultimate goal, um, though I've never quite thought about it in those terms.
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But so I'm a cop's kid, right?
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I grew up around the kitchen table.
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My dad was a law enforcement officer for 30 years, and by all accounts, he was a pretty extraordinary one, really focused on the service aspect of the work and serving his community well and right to the best of his ability.
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Um and I in that I also saw firsthand, you know, the many ways that the system was not serving him.
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You know, he's been retired for, gosh, over 20 years now, but there are still times when he screams in his sleep, you know, and I saw how much he gave to the profession and the communities he served and how little the profession seemed to care about him and the people he was working alongside.
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At the same time, I also grew up in St.
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Petersburg Fora, which at the time was highly segregated.
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And in addition to sort of seeing how the system was failing the people who were working within it, I was also seeing how the system was failing massive segments of our community and the reality of the people that my dad was interacting with most often, the reality of people living on the south side of Central Avenue in St.
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Petersburg.
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That was a very different reality than the one that I had on the north side.
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And sort of understanding those two truths and those two realities simultaneously, I think really kind of activated a desire in me to acknowledge that those failures are systemic and acknowledge that we have to address those failures simultaneously.
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I think if I had to pick a goal for the work that I do, it would be really helping people understand that the vast majority of us want the same things.
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I think when you enter into the space of trying to improve the police profession, you have, you know, one camp that is very squarely in the back of the blue, right?
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We have to give our officers as much as possible.
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And I do think we are there's plenty of improvements we could make in supporting our public safety estimates and the people that work within it.
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But then there's another camp of the people who are seeing the disparities that are happening in this country, that are seeing a lot of the atrocities, that are making headline news.
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And they want some pretty significant improvements as well.
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And a lot of times these camps aren't coming together.
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But the underlying issues that are driving the bad outcomes that they seem to care about most are often the same, right?
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Our police officers are often exposed to the same trauma that some of our most impacted communities are exposed to, for example.
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And I think really finding those opportunity points where it's kind of the rouleau of that then diagram of interested parties and improving the public safety system is really what's been driving me, especially in the last few years.
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Do you have any sort of overarching ambitions, thoughts, uh things that you've learned about how you can bring those two communities together?
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Yeah, I will say I think a lot of the lessons I've learned most recently on this is from my time on the faculty at the University of South Carolina Law School.
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So we have a program there.
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It's called the Excellence in Policing and Public Safety Program.
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And it's funded by the state.
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And our goal is to improve the sort of educational opportunities for current and rising law enforcement.
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And one of the things that's become really clear in that work is that we focus a lot on things like constitutional policing, for example.
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I'm sure you've been in like dozens, if not hundreds of rooms, right, where people are talking about the importance of constitutional policing.
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I talk to a lot of cops and they're like, I'm pretty sure that's just policing, right?
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But uh, it's sort of seen as this North Star.
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But when you get down into it, the constitution is an agreement of negative rights.
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All it does is tell cops what not to do, right?
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And there's actually very little out there in terms of a north star for police, in terms of what they should be proactively doing for the community in a way that's empirically supported, in a way that actually understands the disparate impacts that might result from those particular decisions, in a way that provides some type of democratic accountability, right, to the people that they're serving.
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And because there's so little out there, I actually feel very strongly that it's low-hanging fruit.
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Because when you start talking about public safety in those terms, most law enforcement are hugely supportive of that, right?
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They want the reason why they joined the profession is not to not violate someone's rights, right?
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That's not what gets people out of bed in the morning.
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And in many places, the communities that we're talking to are also there saying we want to proactively be but be a part of the solution.
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We want to co-produce public safety alongside you.
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So I do think there are a lot of opportunities to start from that place of like, this is a shared responsibility for all of us.
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And let's think through together collaboratively how we're actually going to produce these outcomes in your community.
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So when you're with DOJ, I kind of want to, I guess, do this chronologically, go through all of the things that I really just I want you to talk about and discuss with.
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You created or helped create leads.
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Can you walk through what that is?
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Because constantly I'll hear, you know, lead scholar and things like that.
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I don't think until I realized that you were the brains behind that.
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So I'm, can you talk through that and why it's so important and what it's accomplished?
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Yeah, happy to.
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And actually, you know, one of the major sources of inspiration for that was my father.
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So my dad, I always call him like the original police nerd.
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He worked in St.
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Petersburg under Gerald Stevens, who, of course, as chief made some huge strides in terms of sort of research partnerships.
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But he also got an NIJ grant before, which I didn't find out until after I interviewed NIJ.
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I was like, thanks, Dad, that would have really helped in my interview process.
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My dad had a grant from you guys 30 years ago, but he was a coker, right?
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So he developed paperless information systems totally online.
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It was the pistol project.
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It was basically software that allowed law enforcement officers to input their incident reports into tandy laptops that were hard screwed into the top of their cruisers, like where an MDT would be now, right?
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So that was his sort of OG nerdness.
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But I remember I was at an IECP.
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It was my first IECP in 2012.
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And there was this FRINT research awards that were happening.
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Jeff Albert and his team won for something.
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I forget what, it's Jeff, you know, it could be any number of things, but he had like half a dozen of the research assistants who probably done all the work up there with him to accept that award.
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And immediately next to him was Renee Mitchell by herself, who was at that time a sergeant with uh police department in California.
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And she had run in RC completely independently by herself while she was a sergeant in Sacramento.
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And it really struck me that, you know, it reminded me of how my dad approached his work, but it also struck me that there must be other law enforcement officers out there who are working to improve the practice of policing, leveraging data and science, and they are probably going at it alone, just like my dad did, just like Renee was doing on that stage.
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And so I had the opportunity to connect with Jeff Aklart afterwards, and I was just sort of pointing out, you know, just the remarkableness of somebody like Renee getting this work done.
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And in his experience, he found that when he was partnering with research agencies, there was always one or two officers who had gotten their master's or were independently going after their PhD, who really saw the unique value of empirical evidence in affecting their public safety mission.
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And so Jeff and I, it's always dangerous when you're with Jeff at an IUCP.
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He's got a lot of ideas, right?
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So we sort of joined forces and proposed something to Greg Ridgway, who was the director at the time.
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And it started as very simple.
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We're gonna find 10 law enforcement officers across the country who understand the unique value of data and science and who are doing something with their positional authority to advance its integration into police practice.
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We had 10 scholars that first year, and all we did was use some leftover money from a line item appropriation and travel them to IECP.
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I think the first year we spent$30,000 on the leads program.
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And in seeing those officers, in seeing how they thought about the profession, it was very clear that this isn't just about supporting the professional development of sort of nerdy cops, right?
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It really is trying to elevate the next generation of police leaders to lead from the front with a commitment to data and science.
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So it just grew exponentially from there and it's grown into a multi-million dollar project.
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I know there's sort of been a little bit of a transition uh depending on administrative priorities.
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So I hope to see that it continues in the future.
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But it just celebrated its 10th year, I think two years ago, under Nancy Luvine's leadership at NIJ.
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It's been really heartening to see just sort of like that outsized impact that so many of these scholars have had.
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Um I think it also it's heartening to see that the field more broadly is really ready for an orientation towards data and science as the way forward.
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Do you have any success stories or favorite outcomes or anything like that from that program?
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I thought you were gonna ask five favorite people, and I was like, Jeff, not exactly.
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And my five least favorite scholars are.
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So one of the things that I love about that project is that if you are the kind of law enforcement officer, I just anecdotally speaking, I don't know this for certain, right?
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But if you're the kind of law enforcement officer who's gonna go out of your way to get educated about research and data related to your profession, to go get the master's on your own time and your own dime, or maybe go get your PhD.
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You're also the kind of person who is hyper-focused on making things better.
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And so there's so many of these sort of like side efforts that have grown out of the program.
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So I think about Sean Barnes, who was recently the chief in Seattle, he came alongside Tarek McGuire, who's now the chief in Alexandria PD, and Obed Magney, who was an officer with the Sacramento Police Department.
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They got together, they were all lead scholars across different classes.
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And in the aftermath of the police murder of George Floyd, they walked the 54 miles from Selma and Montgomery to really sort of try to close the gap between police and communities they served through the lens of black officers.
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They paid for their own documentary and film crew.
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They are developing a curriculum to sort of support those types of discussions.
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And it really just came from a shared orientation around wanting to leverage data to make the profession better.
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Similarly, the 30 by 30 initiative actually grew out of the Lead Scholars program.
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Um, there was a pretty decent representation of women in the program.
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They got together on margins of IACP and they started to identify some common challenges, spearheaded by Yvonne Roman, who's the former chief of Newark Police Department.
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And that really grew out of the passion of the Women in the Lead Scholars program.
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And I think there's a lot of those stories that sort of continue to happen and crop up here and there.
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But the other thing that I really am affirmed about in that program is seeing that many of the scholars have gone on to be police executives.
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And that to me gives me reason for hope that sort of that unique approach to the profession is one that's that's valued more broadly.
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Nerds can succeed professionally.
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Yes, yes, yes, nerds finish first.
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Have you seen?
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I I've been asking this question for a few months, but uh I want to fit it in here.
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Have you seen a role for AI in either improving the research or improving the efficiency of officers?
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I'm fully aware that, you know, all the downsides potential of generative AI from a coding perspective, like if your dad had access to claud code, you know, what could he have done in a cruiser in the 1980s?
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Like, so is that something you've seen or is that more aspirational at this point?
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That is a chilling question.
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My dad with access to cloud code.
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I can't even imagine.
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I he's luckily retired drinking a mortgage in a boat somewhere.
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So I don't have to worry about that.
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But yeah, I mean, so Jeff, you've stumbled onto one of my favorite areas to expound on and sort of like the place of AI and all of this.
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So yes, I think that there's the obvious efficiencies.
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I think there's the obvious limits in terms of like people's understanding of how to prompt well.
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That's one thing that I think is really holding a lot of people back, but I think that will also get better with time.
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So, yes, of course.
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And I think a lot of what you'll see too is that you might have some police executives who understand the importance of something like a basic RCT, right?
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Or even some like pre-post assessment of, you know, looking at data related to policy changes and implementation and training and all of that.
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And I do think having AI at their disposal, especially if they don't have strong existing research partnerships, is probably going to be helpful if they know enough about how to use it well and right.
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And I don't know if that support system yet exists.
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And I mean that broadly, not in university systems, among police executives, the whole nine.
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I love the idea of really having folks who are a few chapters ahead of other people in the textbook, like I am assuming.
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And actually, I'm very confident that you are one of those people, right?
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But using that knowledge to help law enforcement officers understand, especially in positions of leadership, how they can leverage the tech in the absence of having a master's student at their disposal, for example.
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I think that's great and really exciting to think about.
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Um, I'm also affirmed that it is an application of AI that isn't just about making the status quo more efficient, which is what I think most people are talking about when they talk about AI and policing, right?
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It's like, how can we do what we are currently doing better and cheaper?
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And that seems like a question to ask, but by far not the most important one.
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Right.
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I think there aren't enough discussions about so most of it focuses on what we can use AI for in the status quo and not how does the role of public safety need to transform in the age of AI.
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That's a different question.
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I think things that we sort of took for granted as not possible, like having a more granular understanding of community sentiment and perspectives, that's within the realm of the possible now.
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So if you aren't sort of mindfully integrating your community perspectives in the cost-benefit analysis that you're doing about procurement decisions, for example, you might be able to do that in a different and more meaningful way now.
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I also think about sort of the transformative potential of some of these systems around issues of fairness and equity and how many agencies are just operating blind from a data analysis perspective.
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And you have to wait until something really bad happens before you get the support that you need to look at your data and look for patterns and trends that might be problematic.
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And this could really level the playing field there and set agencies up to be much more proactive in terms of how they use their data to understand the impact of the decisions that they're making and the discretion that their officers are using.
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So that to me is super exciting.
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I've yet to see somebody get out in front, Jeff.
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I hope that's what you're doing in part, but like to get out in front on some of these issues and really guide those discussions because I think without some intention of the people who know best, like yourself, all we will see is just how do we keep doing the same things, but cheaper and faster.
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And that seems to me like a huge lost opportunity.
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It feels like, and again, I'm not on the inside in a lot of these conversations that are sort of happening at the agency level.
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It feels like AI is the tool that's like, hey, it can help make us a poster that shows we're doing a neighborhood walk on Tuesday night.
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And the generative, artsy part of AI that I think is the far less interesting and less useful part is the way that at least the public sees how police departments are using it.
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Are you aware?
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Are there places that are using it for even just the efficiency part, to say nothing of the sort of the dream of having it think of new things and new ways of doing things?
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Yeah, I mean, that's a good question.
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There's a couple areas where I think there's traction externally from like vendors who are supporting these types of services.
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So like AI and report writing.
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Obviously, like we see that a lot.
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And Dr.
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Adams out of South Carolina has some research on that about whether or not Yeah.
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Oh, you're sure you talked about it about whether or not it's actually there's any efficiency gains there.
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Something similar that I've seen is sort of building policy partners for around leveraging AI.
00:17:39.599 --> 00:17:46.319
So helping agencies understand, like, okay, you train a guidance engine on what does good practice look like for X policy.
00:17:46.559 --> 00:17:54.960
Agencies upload their policy, the AI understands, like, okay, in this state, you have this particular framework that you have to navigate.
00:17:55.119 --> 00:17:57.039
Here's what research suggests is most important.
00:17:57.119 --> 00:17:59.039
Here's specific language I would add to your policy.
00:17:59.200 --> 00:18:01.599
That actually feels like that could be pretty significant.
00:18:01.680 --> 00:18:03.839
And I do think ICP is building a policy partner.
00:18:04.000 --> 00:18:07.119
I think there's a couple other organizations that are working on building policy partners.
00:18:07.279 --> 00:18:18.559
The other thing I've seen that I'm actually really excited about, um, and in South Carolina, we've been looking for some federal funding to try and do this, but the application of AI-enabled training for skill building.
00:18:18.960 --> 00:18:23.920
So one of the things that we see, of course, if it's a skills-related training, you go through that training once.
00:18:24.000 --> 00:18:39.200
Maybe you're in the academy, maybe you're in patrol, but there's just not enough time and enough money to take people off the streets to practice those skills, especially around skills like related to emotional regulation or interpersonal interactions, maybe even de-escalation, anything that's largely verbal.
00:18:39.440 --> 00:18:49.279
There's um a couple of startups that are really exploring the use of um AI-enabled training where they train the guidance engine on the types of different responses that you would want to see.
00:18:49.440 --> 00:19:00.240
So uh they will give the officer certain prompts, the officer responds in a way that hopefully reflects what their training is, and then the AI sort of training coach would sort of support them directly, meeting them where they are, all that stuff.
00:19:00.400 --> 00:19:07.119
And there's huge gains that are happening sort of in the higher education space about actually designing curriculum direct for a student in terms of how they learn.
00:19:07.279 --> 00:19:08.400
Are you an auditory learner?
00:19:08.559 --> 00:19:09.599
Are you a visual learner?
00:19:09.759 --> 00:19:18.000
You know, how can we actually assess whether or not you've had the knowledge transfer that we're after using a test that actually meets you where you are in your learning style?
00:19:18.079 --> 00:19:20.480
I mean that it's it's happening very, very quickly.
00:19:20.640 --> 00:19:29.200
And you could see a world where those types of things are brought into police training in a way that um just sort of raises the bar for the recency of skills-based training in particular.
00:19:29.359 --> 00:19:32.799
But like I said, that's all it's super, I haven't seen anybody actually do it yet, right?
00:19:32.880 --> 00:19:33.759
It's it's super new.
00:19:33.839 --> 00:19:38.880
I know there's like a bunch of us who are trying to like get the momentum there, but I do think it's gonna take a lot of time.
00:19:38.960 --> 00:19:44.559
I think there's an appropriate amount of mistrust around AI technology too, both among communities and also among police.
00:19:44.640 --> 00:19:50.480
So it, you know, it might take a little bit of time to fully realize remotely the kind of potential we're we're talking about here.
00:19:50.799 --> 00:20:02.880
One of the issues, not completely related to AI, but sort of in the tech space that a lot of police departments, I think, and especially from the vendor side, are having is mistrust with surveillance and especially with what Flock is going through.
00:20:03.039 --> 00:20:05.680
Um I think some of it's probably rightly earned.
00:20:05.759 --> 00:20:21.440
Um, I mean, New Orleans, neighboring Jefferson Parish, just had an officer that was fired and potentially arrested for, I think, 700 misuses or 7,000, some some ridiculous number of misuses of ALPR um cameras to track his ex-wife.
00:20:21.759 --> 00:20:38.319
So is this something that police can better communicate how not just ALPR, but all of these new technologies are being used rather than, hey, we've got this magic surveillance box that will help us solve all the crime.
00:20:38.880 --> 00:20:41.279
Jeff, this is my second favorite thing to expound on.
00:20:41.359 --> 00:20:43.200
So it's 30 by 30 this.
00:20:45.359 --> 00:20:55.440
So I think there's two things we need to think about when we think about, um, especially, you know, a lot of the increased attention around technology like Flock after the Washington Post article.
00:20:55.680 --> 00:20:58.799
There's the fundamental misuse of the technology, right?
00:20:58.960 --> 00:21:03.359
And that to me feels very much like a police accountability lens, right?
00:21:03.680 --> 00:21:14.799
We need infrastructure in place to appropriately control the use of the technology so that it is not violating people's privacy or civil liberties or civil rights, and that it is not being used to advance personal objectives, all of those things.
00:21:14.960 --> 00:21:17.680
This to me feels like a police leadership issue and accountability, right?
00:21:17.920 --> 00:21:22.240
Figure out how to get your department in order so those things aren't happening with the technology that you procured.
00:21:22.960 --> 00:21:27.759
The other bucket that I think needs to be wrestled with is the civil liberties conversation.
00:21:27.920 --> 00:21:33.759
I think a lot of times people are conflating the two, but one can be solved right now by getting your department in order, right?
00:21:33.839 --> 00:21:39.519
Like this is completely within a purview of a chief to make sure that they've got structures in place that technology is used appropriately.
00:21:40.000 --> 00:21:49.440
I think the general public is surprised at the capability of the technology, regardless of whether it's used appropriately and legally or not.
00:21:49.839 --> 00:21:51.759
And that feels like a different conversation.
00:21:51.920 --> 00:21:59.839
And I think the people that lead that conversation really well are the people who are at the center of the Venn diagram between technologists, police practitioners, and community actors.
00:22:00.079 --> 00:22:01.119
Advocates, right?
00:22:01.200 --> 00:22:06.319
I understand why people have hardline abolition stances on surveillance technology.
00:22:06.480 --> 00:22:07.839
I understand where that comes from.
00:22:08.000 --> 00:22:12.559
And there certainly is a history of use and misuse and abuse in this country that could drive that.
00:22:12.640 --> 00:22:18.480
And especially when the technology was first coming out, there were a lot of disparities in accuracy around people of color, for example.
00:22:18.640 --> 00:22:21.839
So it's compounding a lot of the disparities that already exist in the system.
00:22:22.079 --> 00:22:33.599
As the technology is advancing and as the technology is improving, I think the people who are making the most progress are the people who are keeping up with that advancement and their concerns are appropriately aligned to where the technology currently sits.
00:22:33.759 --> 00:22:40.880
The people who are talking to the technologists are the people who understand that ultimately, like we live in a highly capitalist society and this is a procurement issue.
00:22:41.119 --> 00:22:50.319
So if we can get communities and police departments to say, we will not buy X technology unless it has these sort of protections built in it, then that feels like a huge win.
00:22:50.400 --> 00:22:57.599
But that conversation can only be had when all three of those parties are coming together at the table at the same time, which is really hard to facilitate and do.
00:22:57.839 --> 00:23:01.519
I used to work at the policing project out of NYU School of Law under Barry Friedman.
00:23:01.759 --> 00:23:21.839
And I think he's done an extraordinary job of having conversations that are oriented toward responsible technology use where it's using the constitution appropriately as a floor, where it's appropriately contemplating, you know, the very serious concerns that the public has about that use while also understanding there's a legitimate public safety rationale for a lot of what's out there.
00:23:21.920 --> 00:23:25.519
And if it's used appropriately, it can dramatically improve public safety.
00:23:26.000 --> 00:23:39.039
How can Flock or police departments or, you know, just researchers better argue or better articulate the effectiveness of these technologies?
00:23:39.200 --> 00:23:50.079
I know Ian Adams has a preprint out that looks at Flock potentially finding a decrease in property crimes along with uh was it vehicle burglaries or auto thefts when the cameras went up.
00:23:50.240 --> 00:24:07.599
But if you read Flock's website, it like as a data analyst, it drives me insane that it's like uh, you know, we helped in 20% of clearances, or there's a line that like our survey of 33% of agencies found a 30% increase in property crime clearances.
00:24:07.680 --> 00:24:11.119
It's like all of these words that could have multiple meanings.
00:24:11.440 --> 00:24:18.880
How can we understand how these technologies are useful or articulate or do the research that these technologies work?
00:24:18.960 --> 00:24:21.920
And if they don't work, find the research that says that.
00:24:22.240 --> 00:24:22.480
Right.
00:24:22.640 --> 00:24:24.400
Jeff, I love this question.
00:24:24.480 --> 00:24:28.319
And I feel like my answer is not going to be helpful for jurisdictions that are wrestling with this now.
00:24:28.799 --> 00:24:36.799
But I think one of the problems is that the public only starts to pay attention to the capabilities of these technologies when they're misused.
00:24:37.119 --> 00:24:37.440
Right.
00:24:37.599 --> 00:24:45.839
So you already have this lens where people are sort of oriented away from the utility of the tool because their primary concern is around its misuse.
00:24:46.079 --> 00:25:09.359
If you have that approach to democratic accountability, if you have that approach to sort of the co-production of public safety to bring it back around what we were talking about at the top of the hour, you know, you have members of the public who care about these issues and are going to be your likely detractors at the table with you at the beginning when you're conducting your cost-benefit analysis so that they're exposed to the utility of the tool and why you want to procure it, right?
00:25:09.440 --> 00:25:14.799
The primary reason why you procure the technology is not so some nefarious officer can go stalk their ex-girlfriend, right?
00:25:14.880 --> 00:25:22.640
It is because you legitimately believe that for the money you have at your disposal, this is the best possible way you could advance your public safety mission based on those capabilities.
00:25:22.799 --> 00:25:35.599
And to have the communities that you're serving at the table for those types of discussions and to have them fully understand from the beginning the transformative potential of that tap on your public safety mission, I think that sets you up for much better discussions if and when something does go wrong.
00:25:35.920 --> 00:25:37.839
So I think that's what's been missing from the equation.
00:25:38.000 --> 00:25:43.599
That of course doesn't help if a jurisdiction's already knee deep in surveillance technology and has not had a conversation with their community.
00:25:43.839 --> 00:25:45.759
Which is where the conversation is now for most places.
00:25:45.839 --> 00:25:48.319
So we've already invested in it, should we have.
00:25:48.640 --> 00:25:49.039
That's right.
00:25:49.200 --> 00:25:53.359
And and that's where I think separating that into those two buckets is really helpful, right?
00:25:53.519 --> 00:26:00.559
If you have people who are coming to you with legitimate concerns about how the technology is used, that's the purview of your police chief.
00:26:00.640 --> 00:26:11.279
Put your police chief out there, talk about how you're going to improve the protections, talk about what industry best practice looks like, talk about the accountability structures that you have in place, and stay away from the civil liberties conversation if that's not where your community's going.
00:26:11.359 --> 00:26:16.720
But a lot of the public statements I'm seeing are conflating the civil liberties issue with the appropriate use and accountability issue.
00:26:16.880 --> 00:26:20.319
And I think that's just failing to help anyone understand what good should look like.
00:26:20.640 --> 00:26:23.519
So let's move to what I hope is your third favorite thing to talk.
00:26:23.680 --> 00:26:24.960
You mentioned 30 by 30.
00:26:25.200 --> 00:26:25.759
What is that?
00:26:25.920 --> 00:26:27.839
Talk the audience through what that is.
00:26:28.160 --> 00:26:30.240
Well, okay, that might actually be my favorite topic.
00:26:30.480 --> 00:26:43.920
So um 30 by 30 is a national, now international grassroots movement that, as I mentioned, grew up out of uh women in law enforcement uh that I co-founded alongside Chief Yvonne Roman, who I mentioned earlier back in 2021.
00:26:44.160 --> 00:26:48.240
And our goal is to improve the representation and experiences of women in law enforcement.
00:26:48.400 --> 00:26:54.240
We are doing that actually not because we are ardent gender equity advocates, but because we care so deeply about public safety.
00:26:54.319 --> 00:27:09.039
And there's a strong and growing body of research around the unique value of women officers, everything from less force and excessive force to improved outcomes for crime victims, especially victims of gender-based violence, named proportionately less in complaints and lawsuits, more trusted by communities they serve.
00:27:09.119 --> 00:27:09.680
The list goes on.
00:27:09.920 --> 00:27:15.599
But when we started this work, women were only 12% of sworn law enforcement and only 3% of police chiefs.
00:27:15.680 --> 00:27:18.400
And those numbers hadn't changed much in decades.
00:27:18.799 --> 00:27:22.720
So what we did is we looked at the research from analogous industries.
00:27:22.799 --> 00:27:26.000
We looked at what little research was there in American policing.
00:27:26.240 --> 00:27:38.720
We pulled together a pretty robust advisory board and we came up with solutions and strategies that are known low cost that should improve both the representation and experiences of women in police departments.
00:27:38.880 --> 00:27:42.960
We have over 400 agencies across the country, including almost every major metro.
00:27:43.119 --> 00:27:55.920
And our approach was recently replicated in Canada under the uh banner of 30 Forward, and they are now less than a year in every province and are looking at probably full national implementation within the next few years.
00:27:56.160 --> 00:28:01.759
They don't have 18,000 agencies like us, so they get there a little bit more quickly, but there's a lot of reasons for hope there.
00:28:01.920 --> 00:28:09.279
Because we are super committed to being data-driven, Jeff, you'll be happy to hear that our participating agencies are required to report data to us in six-month increments.
00:28:09.440 --> 00:28:13.519
And we got some funding from NIJ in the last administration to look at how that data changed.
00:28:13.680 --> 00:28:20.799
And we found that after two years with the 30 by 30 program, agencies saw a 28% increase in women in their recruit classes.
00:28:20.960 --> 00:28:34.880
So we're very optimistic that the strategies we're focusing on, everything from recruitment strategies to improving the accuracy of assessments to meeting women's specific health needs in the department, something as basic as making sure they have a ballistic resistant vest that fits them.
00:28:35.119 --> 00:28:38.960
All of those things in aggregate seem to be making a pretty big difference.
00:28:39.039 --> 00:28:41.119
So we have a lot of reasons for hope there.
00:28:41.519 --> 00:28:47.759
You literally answered all of the follow-up questions that I'd written down about how do you measure success and are you seeing it?
00:28:48.160 --> 00:28:50.480
Why don't women become police officers more often?
00:28:50.960 --> 00:28:53.119
Well, Jeff, I will say one thing I didn't mention.
00:28:53.359 --> 00:28:58.720
While we have every reason to be hopeful, it is a fraught time to be doing that work in this particular moment in history.
00:28:58.880 --> 00:29:04.319
We lost all of our federal funding, and that's probably not a surprise if anybody's been paying attention to the headlines.
00:29:04.480 --> 00:29:10.559
And one of the things that happened has happened is that the discourse against anything that's trying to promote women has gotten quite loud.
00:29:10.720 --> 00:29:13.519
So I don't want to pretend like everything is like hunky dory and rosy.
00:29:13.599 --> 00:29:17.920
I mean, I think it's working, and I think we have a lot of reasons to be hopeful about how it changes in a field.
00:29:18.160 --> 00:29:25.599
But even as that sort of national macro level discourse is getting louder, the field's commitment to the work is also getting louder.
00:29:25.759 --> 00:29:36.319
So we've only lost, I think, two agencies at the state and local level from our participating agencies, even as the federal support went away, even as some of these sort of shots across the ballot come for us.
00:29:36.480 --> 00:29:49.200
So, yeah, overall, despite it being a complicated space to work, I think just as the Lead Scholars program gives me a lot of hope about what the profession actually values in their leaders, the 30 by 30 program is giving me that same hope as well.
00:29:49.599 --> 00:29:54.480
And 30 by 30, does that stand for 30% by 2030?
00:29:54.880 --> 00:29:55.519
Great question.
00:29:55.680 --> 00:29:56.960
If I could rename it, I would.
00:29:57.039 --> 00:29:59.839
So, Jeff, it It seems so far away, didn't it?
00:30:00.160 --> 00:30:01.039
Friend, that part.
00:30:01.119 --> 00:30:01.599
It did.
00:30:01.759 --> 00:30:02.880
It did in 2021.
00:30:03.039 --> 00:30:06.559
But there's there's a couple of like complications that have grown out from the name.
00:30:06.720 --> 00:30:10.079
I think it contributes to people's misunderstanding of what we're doing and how we're doing it.
00:30:10.319 --> 00:30:18.880
But the 30%, it is 30% women recruits by the year 2030 is was our sort of overarching goal when we launched.
00:30:19.119 --> 00:30:23.279
And the reason why we landed on 30% is because there's a theory of representative bureaucracy.
00:30:23.359 --> 00:30:34.799
Um, it actually reminds me of Malcolm Gladwell's tipping point stuff a little bit, but until you reach 30% of an underrepresented group in a public institution, that group can't impact the institution for their benefit.
00:30:35.039 --> 00:30:39.440
So anything under 30%, underrepresented groups are just assimilating to survive.
00:30:39.599 --> 00:30:46.240
So all of that unique value of their unique perspectives is lost as they try to just sort of fit in with the majority group.
00:30:46.480 --> 00:30:51.519
30% is when you really start to see the benefits of broad representation at the organizational level.
00:30:51.599 --> 00:30:53.119
So that's what we were going after.
00:30:53.359 --> 00:31:03.680
But, and this is a really important, but that is the overarching side effect that we hope will happen after you do the things that are about sound agency management.
00:31:03.839 --> 00:31:04.079
Right.
00:31:04.240 --> 00:31:09.759
So we're not going out to agencies and saying, like, find binders full of women and just go hire those ones, right?
00:31:09.920 --> 00:31:11.680
Or just go out and promote a bunch of women.
00:31:11.839 --> 00:31:25.759
Like it's about making sure that you're recruiting for what the job actually requires from pools that likely have those skills that tend to be underlooked, like nursing majors and psychology majors and social workers who also have an overrepresentation of women, right?
00:31:25.839 --> 00:31:37.920
It's about making sure that your assessments in many places that haven't been changed in 70 years actually measure what the job requires, especially if they happen to be disproportionately washing out your women candidates, right?
00:31:38.079 --> 00:31:47.359
And then it's about once you get those women in the door in this male-dominated space, are you intentional about having structures in place to make sure their unique needs are met?
00:31:47.519 --> 00:31:48.400
That's it, right?
00:31:48.480 --> 00:31:52.559
And I think when people hear 30 by 30, they think, oh, it's just a quota thing.
00:31:52.720 --> 00:31:57.440
Oh, it's just a token thing, oh, they're just quote unquote lowering standards to get more women in the door.
00:31:57.599 --> 00:31:59.039
Nothing could be farther from the truth.
00:31:59.119 --> 00:32:10.160
And we actually think approaches to diversity that use those mechanisms are are highly problematic, including and especially for the women who are currently working in the department who got there, you know, in the same way that everybody else got there.
00:32:10.319 --> 00:32:21.119
So I can, if we do it all over again, honestly, Jeff, I would not call it 30 by 30 because I think it sort of gives people a false understanding of how we approach the work by being really honest.
00:32:21.359 --> 00:32:22.799
All right, listeners don't tell anybody.
00:32:22.880 --> 00:32:27.759
We just called it 30 by 30 because we thought dudes would confuse it with 3430, that like ESPN thing.
00:32:27.839 --> 00:32:30.240
So they'd be like naturally inclined to engage with it anyway.
00:32:30.480 --> 00:32:32.960
They're like, oh, that sounds like something fun and interesting that I'd watch on a Sunday.
00:32:36.799 --> 00:32:37.119
Exactly.
00:32:37.279 --> 00:32:38.960
Then it's like, psych, it's gender equity.
00:32:39.119 --> 00:32:41.680
So that was that was really the long-term strategy there.
00:32:41.759 --> 00:32:44.160
And it didn't end up working out the way we thought it would.
00:32:44.720 --> 00:32:49.119
So I guess we sort of bring this back to an eventual conclusion.
00:32:49.279 --> 00:32:59.519
I'm impressed just talking to you about like not one, but like multiple grassroots stuff that you started that became things for lack, lack of a better word.
00:32:59.839 --> 00:33:00.480
How do you do that?
00:33:00.559 --> 00:33:07.599
How do you accomplish like taking an idea and turning it in, you know, first a thing and then a big thing?
00:33:08.079 --> 00:33:09.119
I appreciate you saying that.
00:33:09.200 --> 00:33:09.759
I mean, it's interesting.
00:33:10.000 --> 00:33:12.480
I still feel like I'm like two years out of school, just like scraping by.
00:33:12.640 --> 00:33:16.160
So it's nice to have these types of conversations where you realize like I'm actually quite old, right?
00:33:16.240 --> 00:33:17.519
And there's a lot of things that have happened.
00:33:17.759 --> 00:33:18.559
Well, we're young.
00:33:20.799 --> 00:33:21.519
In the last 15 years.
00:33:21.599 --> 00:33:25.279
But if I could find the through line in this stuff, it's about everybody else who isn't you, right?
00:33:25.359 --> 00:33:27.119
It's about finding those good people, right?
00:33:27.279 --> 00:33:32.960
Leads wouldn't happen if Jeff Alpert didn't think it was a good idea, if Gridwid Greg Winjway didn't think it was worth investing in, right?
00:33:33.119 --> 00:33:42.079
30 by 30 wouldn't have happened if Savan wasn't so committed to making sure this work happened under her tenure, or if someone like Carlton Moore, when he had positional authority, PGA, didn't invest in it, right?
00:33:42.160 --> 00:33:54.640
It really is about sort of finding those issue areas that are so empirically supported and so logical that you can't help but fight for them and then surround yourself with the people who really believe in the mission and the work.
00:33:54.880 --> 00:34:00.960
You know, I there's so many inspiring stories and, you know, books written about people who like go at stuff alone, right?
00:34:01.039 --> 00:34:06.880
Like so many of the changes that have happened in American policing are because somebody just like completely sacrificed themselves to make it happen.
00:34:06.960 --> 00:34:12.800
I think about Penny Harrington, who was the first woman chief of the Portland Police Bureau, first woman chief in this country, right?
00:34:12.960 --> 00:34:14.960
She was appointed chief in 1985.
00:34:15.199 --> 00:34:17.199
Dozens, if not hundreds, of lawsuits.
00:34:17.280 --> 00:34:19.840
Like the mental health toll was extraordinary, right?
00:34:19.920 --> 00:34:24.880
And she really sacrificed her life to be the example of how things could be better.
00:34:25.119 --> 00:34:41.440
When you're doing the systems level work, which is like what Leeds kind of is and like what 30 by 30 is, and what even what something like 54th mile is, it really is about building as broad of a network as possible of people who understand the importance of the work and are willing to use their positional authority to make it happen.
00:34:41.599 --> 00:34:48.639
And in that respect, I think that policing is a really beautiful space to do that work because there are so many people who are oriented towards the good.
00:34:48.800 --> 00:34:52.480
It just happens that sometimes there's not a North Star of what good looks like in that particular moment.
00:34:52.559 --> 00:35:01.280
So if you can sort of come alongside with the strength of the research that people like you produce and help people understand what the future state could look like, it's a great way to spend a day, right?
00:35:01.360 --> 00:35:04.480
And I think that's really why we've been able to have like have those shifts.
00:35:04.559 --> 00:35:12.079
And when I think about like the people in the space that I mean, I'm sure many of them are your guests or will be your guests, I think that is a shared thing, right?
00:35:12.159 --> 00:35:16.480
They have a broad network of people who are willing to do the work and speak out for what's right.
00:35:16.719 --> 00:35:27.119
Yeah, that's the challenge is having uh such a long list of people that I want to talk to that I'm like doing for over a year now and still have like 70 people on the list of potential futures.
00:35:27.199 --> 00:35:38.960
So is there an idea that you're like, I really want to do X, but haven't gotten around to it, or the technology's not there, or you know, funding wasn't available when I first pitched it.
00:35:39.039 --> 00:35:42.239
Is there something that you really want to do that just hasn't happened?
00:35:42.320 --> 00:35:48.079
I'm not trying to steal the idea or, you know, take your IP here, but Jeff, you can steal it.
00:35:48.159 --> 00:35:49.119
I'm so tired.
00:35:49.519 --> 00:35:50.960
I'm gonna have it done.
00:35:51.199 --> 00:35:59.760
Um uh so it's not an idea, but I think it's a problem that if we solve, a lot of downstream challenges will be addressed as well.
00:36:00.079 --> 00:36:03.199
And that is, I feel like a broken record when I talk about it.
00:36:03.360 --> 00:36:10.400
But the lack of validation around hiring and promotional assessments and public safety, I think is a really big problem.
00:36:10.800 --> 00:36:15.920
And I think other industries have figured things out that we could borrow from pretty heavily.
00:36:16.239 --> 00:36:31.519
But I think that and I know in most jurisdictions there's a recruitment problem and people are really just trying to get butts and seats, but I don't think there's a sense of what KSAs are actually associated with somebody being an effective law enforcement officer.
00:36:32.000 --> 00:36:39.039
I also think the types of things that we test for and the types of things that we exclude for are also things that are teachable and trainable.
00:36:39.360 --> 00:36:39.599
Right.
00:36:39.679 --> 00:36:52.000
There's like that whole, there's a massive body of literature around this, like in higher education in particular, about, you know, knowledge you can teach, skills you can teach, but you really want to be hiring recruiting for aptitude, right?
00:36:52.159 --> 00:36:53.519
And attitude and habits.
00:36:53.599 --> 00:36:55.280
And those are the people you want to bring in.
00:36:55.440 --> 00:37:03.840
The biggest predictor of somebody's success is their growth mindset, not whether or not they came in knowing six particular subparts of a local regulation, right?
00:37:04.559 --> 00:37:09.920
You want to test if the person can learn, not necessarily if they learned the things that they needed to before they got you.
00:37:10.079 --> 00:37:15.280
And so I think if, and that to me is a seven-figure project, eight-figure project.
00:37:15.360 --> 00:37:24.800
It is a bunch of police professional associations, it is, you know, technologists, it is organizational psychologists, it's educators, it's kinesthesiologists.
00:37:24.880 --> 00:37:29.519
I mean, it's huge and it's multidisciplinary, but that's because the work of policing is multidisciplinary too.
00:37:29.679 --> 00:37:39.119
So if somebody's out there with figures and they just want to throw it at Jeff to assemble a team of people to establish the basic minimum requirement.
00:37:39.440 --> 00:37:40.400
Do anything, really.
00:37:40.559 --> 00:37:40.960
Yeah.
00:37:42.239 --> 00:37:44.960
Just know I'm also interested in being part of that conversation.
00:37:45.039 --> 00:37:47.199
But yeah, I think I don't know.
00:37:47.280 --> 00:37:49.679
I just, it's, it seems like it's like right there.
00:37:49.840 --> 00:37:51.440
It just needs the right person's leader.
00:37:51.599 --> 00:37:55.360
I don't know enough in the space to even begin to talk about it more than everything I just told you here.
00:37:55.440 --> 00:37:59.280
And my hope is that somebody else who knows more just picks it up and runs with it.
00:37:59.360 --> 00:38:01.440
But that just seems critically important to me.
00:38:01.760 --> 00:38:19.599
I think the other thing that is is worth exploring is creating the kinds of tables and spaces that I was talking about earlier, where the people who work within the system and the people who are impacted by that system are actually together for generative conversations about what good could and should look like.
00:38:19.840 --> 00:38:23.360
And I think there's a lot of restorative justice literature that goes into that.
00:38:23.599 --> 00:38:29.920
I think, you know, you have people like Ganesha Martin, who's a bureau chief up in Minneapolis, who's approaching the work of reform through that lens.
00:38:30.079 --> 00:38:31.599
Like there's a lot of reason for hope.
00:38:31.679 --> 00:38:36.880
And I think some people hear something like that and they think it's very soft or very like, oh, that's the heart stuff.
00:38:37.039 --> 00:38:38.719
But it's the human stuff, right?
00:38:38.800 --> 00:38:41.920
These are human systems, affecting humans, hiring humans.
00:38:42.079 --> 00:38:44.320
And if we don't start there, I just don't know how far we'll get.
00:38:44.559 --> 00:38:45.920
Great, great, great ideas.
00:38:46.079 --> 00:38:47.599
This has been such a great conversation.
00:38:47.679 --> 00:38:49.519
I'm so inspired by all of this work.
00:38:49.679 --> 00:38:51.519
And also, you know, there's so much to do.
00:38:51.760 --> 00:38:52.800
What is next for you?
00:38:52.960 --> 00:39:01.119
And the second part of this question is if you had to rank this podcast appearing on this podcast versus Jerry Radcliffe's podcast, where would they rank?
00:39:01.280 --> 00:39:02.480
One versus two.
00:39:02.639 --> 00:39:06.880
So the well, let's do the podcast question first, because that's the most important.
00:39:07.280 --> 00:39:12.079
I mean, it might be the recency of fact, Jeff, but this was you asked me about my favorite things, right?
00:39:12.239 --> 00:39:17.599
I Jerry's was my first podcast, so it'll always have a sort of soft spot in my heart.
00:39:17.679 --> 00:39:19.039
And I don't know, well, I'll just say it.
00:39:19.119 --> 00:39:20.400
I think it made it onto his podcast.
00:39:20.480 --> 00:39:25.360
We were drinking vodka out of paper bags in a public park because it was COVID, so we couldn't be near each other.
00:39:25.519 --> 00:39:34.400
So, you know, there was that sort of uh the tableau of that one is a little bit more interesting than my basement office, but uh I really, really, really enjoyed this conversation, Jeff.
00:39:34.559 --> 00:39:40.079
And I also think your approach to the issues is you and Jerry are complementary but distinct.
00:39:40.159 --> 00:39:44.719
Um, and I really like the places that we went, but you're you're both valuable and special in your own way.
00:39:45.360 --> 00:39:46.639
Very serious face there.
00:39:46.800 --> 00:39:47.119
Yeah.
00:39:48.719 --> 00:39:49.840
And what is next for you?
00:39:50.079 --> 00:39:51.199
Just keep doing what I'm doing.
00:39:51.280 --> 00:39:58.079
I mean, there is so much reason for hope in every single one of the things that we talked about, right?
00:39:58.400 --> 00:40:11.519
And there are so many people who are approaching the work from a lens of how do we do right by everyone, and who have the shared understanding that the best way to get there is the research and data that shows us what good looks like.
00:40:11.679 --> 00:40:13.280
And so I'm I'm really heartened.
00:40:13.360 --> 00:40:14.719
I'm just gonna keep doing what I'm doing.
00:40:14.800 --> 00:40:17.119
I'm very tired, but like, what else are we gonna do?
00:40:17.199 --> 00:40:17.280
Right.
00:40:17.440 --> 00:40:18.800
Like that's sort of what this life is for.
00:40:18.960 --> 00:40:19.840
We'll sleep and we're dead.
00:40:20.000 --> 00:40:24.559
But any one of these issue areas is an area that I think is worth investing time and energy in.
00:40:24.639 --> 00:40:35.440
And I feel super, super freaking fortunate that I have the opportunity and the latitude to choose to invest energy in those spaces, right?
00:40:35.519 --> 00:40:37.199
Uh the tables aren't big enough.
00:40:37.280 --> 00:40:38.800
And I wish we could pull more people in.
00:40:38.960 --> 00:40:40.239
I wish there was more funding.
00:40:40.400 --> 00:40:48.400
I wish, you know, there's more flexibility in funding where these really big innovative thinkers like your Tarek McGuire's, you know, could actually take some big swings.
00:40:48.480 --> 00:40:53.440
Uh, but in the interim, I think we'll proceed in a much more stepwise way and we'll just keep making change where we can.
00:40:53.679 --> 00:40:54.239
Great message.
00:40:54.400 --> 00:40:56.079
Mo, thank you so much for joining the show.
00:40:56.400 --> 00:40:57.280
Jeff, wonderful.
00:40:57.360 --> 00:40:57.840
Appreciate you.
00:40:57.920 --> 00:40:58.880
Thank you so much.
00:40:59.280 --> 00:41:01.440
Thanks for listening to the Jeffalytics Podcast.
00:41:01.519 --> 00:41:07.840
Be sure to subscribe and to learn more, head on over to ahdatalytics.com for more information and previous episodes.
00:41:08.079 --> 00:41:12.320
If you like what you heard, please leave a glowing review, which will help others to discover the show.
00:41:12.559 --> 00:41:14.239
Till next time, I'm Jeff Asher.