Vermont Legislature SmartTranscripts:

[Nate (Committee staff/IT host)]: Live. Alright. Welcome back to

[Rep. Martin LaLonde (Chair)]: the House Judiciary Committee, and we're gonna turn to h three eighty two. And I think we're gonna have a brief history lesson and a little introduction to h three 82 from representative coach Kevin Christie. Thank you. It's good to see you back, coach.

[Rep. Kevin “Coach” Christie (Ranking Member)]: Thank you. I'll I'll try to start, and Susana and her team basically covered a lot of the information that brings us to three eighty two. The thing we need to remember is is that the purpose of three eighty two is to establish a consistent public reporting of the criminal justice data across all Vermont agencies. You know, why does this bill exist? Since approximately 2017, which is a little scary. That's ten years. You know. And as I look around the room, you know, there are some folks that have been involved in that historical component, especially for our committee, because a lot of this work, has been generated out of judiciary. So since approximately 2017, Vermont has undertaken multiple initiatives addressing fairness and disparities in the criminal justice system, you know, including, creation of the Office of Racial Equity, establishing the Bureau of Justice Statistics within that unit and our extensive work around fair and impartial policing efforts. The the key has been our partnerships, and I I will be putting together it's a one page, and what it does is it kinda gives a primer of a number of the agencies and partners that will be testifying today either in paper or in person. H three eighty two also creates standardized reporting expectations so policymakers and the public can evaluate outcomes across the entire system. You know, the core idea, the state has invested in fairness initiatives for nearly a debt decade. And this bill provides the infrastructure to evaluate those efforts using shared facts. So that that that is a key component, you know, of the work. You might also hear in some of the presentations or see in some of the presentations, I should should say the term data lake.

[Rep. Martin LaLonde (Chair)]: The term what, coach?

[Rep. Kevin “Coach” Christie (Ranking Member)]: Data lakes. Remember Okay. That metaphor where our partners have access to this massive amount of data. And it's it streams in in a number of different ways, you know, through the criminal justice system. We see it through VCIC. We see it from crime research group in their work. We see other streams of information coming in to that lake from UVM and now Susana's group. And what three eighty two is looking at is how do we get the guardrails in place so that we, as the the policymakers, can functionalize all of that data that's in that lake, and that's part of what the structure is designed to do within three eighty two. So I guess that at this point, because a lot of the like I said, you know, a lot of the information that we were talking about, you know, has has been covered, you know, as I look at, you know, my notes, and the handouts that have already come and the reports, you know, I mean, you know, there is some incredible information that's been shared with us over the last couple of days. And, you know, now it's time for us to give that structure to what our expectations are for March. Is that hopeful, Martin?

[Rep. Martin LaLonde (Chair)]: Yeah. No. That is. I I appreciate that as an introduction. It's it's kind of part of we're trying to just get a handle today initially on the data needs, what the work is being done, which the Division of Grateful Justice Statistics gave us a good overview this morning. And also wanted to hear So we have received written testimony from three of the four entities that are addressed in March and it has been posted or is being posted. We're still waiting, I believe. Actually, just received the email from the court. So we'll have all four of the written testimony. I asked them to address what data is available, what it would take to provide that data. And so we have that written testimony to give us a good understanding of kind of that leak, as you call it. But wanted to also have Susana weigh in on March relevant to the work that the division is doing, and then asking Pat Attilio to weigh in as far as the need for this data so we can kind of get grounded in why, at least from one perspective, an important perspective, why we need this information. So are you a good coach or can I turn it over to Susana?

[Rep. Kevin “Coach” Christie (Ranking Member)]: You can turn it over to Susana. And, you know, as we hear from her and Pat, depending on what's covered, if there's still additional data that I have, I can share that along the way.

[Rep. Martin LaLonde (Chair)]: Okay. So we'll go we'll go back to you at that point. So over to you, Susana. Thank you for to be available. Really appreciate it.

[Susana Davis (Director, Office of Racial Equity)]: Thank you very much. Again, for the record, Susana Davis, Office of Racial Equity. And I think our remarks could actually be pretty brief. I'll start by saying I am looking forward to reviewing the testimony that you all received in writing from those covered agencies. We'll definitely be looking out for those, one, because it helps our internal understanding of the data landscape in general, and will also help us cross check the information that we receive through our data gap analysis project. And also because I choose to believe that they know their systems right now better than we might. So it will be helpful for us to learn from them what their viewpoints are on this bill. As far as our work is concerned, our review of this bill has led us to sort of bifurcated conclusion that some of the data points that are sought in the bill, it's our understanding that they are already collected or that they exist somewhere. Some of them are not currently collected, and we agree that they should be. And some are not currently collected, we agree that they should be. And it's our view that it would require some significant technological or data infrastructure modernization in order to be able to collect those. So I could go into detail. Our staff person from whom you heard earlier today very helpfully put together a table for us. And I can share that with the committee if you all would like, and it maps out each of these components and our notes on it. I also wanna highlight that RDAP, the Racial Disparities in the Criminal and Juvenile Justice Systems Advisory Panel, has also opined on these data points in past reporting. And the table that our team put together also notes which of these data points had been previously contemplated by the RDAP as well. So that kind of is our general perspective that some of this may already exist. And for those components, what that tells us is that something we already knew, that these data need to be compiled in a way that's accessible to the public and to policymakers. And I don't just mean accessible in the sort of ones and zeros sense, right? Have we technically complied with some legal mandate that something technically exists out there. But I really mean accessible in a way that is digestible for laypersons and that is actually actionable and usable. So, I, Nope. Nope. I'm going to save that from my diary. So that is So that is the perspective that we're coming from with the items that we do believe are already being collected or that already exist. For those components that are not collected or that are not, yeah, that are not collected or that may need additional infrastructure support to be collected and reported on, We again are gonna go back to our technological, our technology assets inventory work and the gap analysis work that we think will be informative at helping to identify what are those modernizations and upgrades that would need to happen. What is the order of operations in order for us to be able to get some of those data points? Some of it may be as simple as, you know, saying, we're moving to a new vendor because we have a system that may not be vendor supported anymore. It could be that. Or it might be something else related to data sharing from certain agencies or the way the data are collected. For example, we know that following a person's trajectory through the criminal legal system, we may have race data or ethnicity data reported for that person by the police or by the courts or by some other entity. But if an entity downstream is reporting that person's race and all of that is being informed by one upstream collection point that may have happened, let's say, as an example, by law enforcement, if that first one is incorrect and future reporting on that person's race or ethnicity data is based on that, then the whole chain is tainted. And so sometimes for some of these, it may not necessarily be as much a question of whether it's public or whether it's collected in a usable system. And sometimes it has to do with the quality of the data as its first input itself. So that's another thing that we really want to consider. The last point that I'll make before I ask my staff if there's anything else that I'm missing is that a big concern for us is I don't know what the correct term is, but swim lane overlap. So there are a few things. I'll give one example. One of the items named in the bill is, in a reporting entity in the judiciary's in the proposed report from the judiciary, it would be looking for pretrial release data, including bail information. And one of the things that we noted is that DOC also maintains some bail data. So in creating a complete record, a complete and accurate record of bail information, which branches of government or agencies are we tapping for those data, and how are we making it a complete picture? And this is one of the areas in which there may be some either duplication or possible redundancy within our system.

[Nate (Committee staff/IT host)]: Pardon me, Susanne, I muted the wrong person. Pardon me, Susanne, unmute yourself and continue, sorry.

[Susana Davis (Director, Office of Racial Equity)]: That's okay, thank you. So it may be the case that there may be some redundancy or duplication, either on the collection side or the reporting side. And so for us, it's really important that we create clarity around whose role is it to collect, analyze, report on, and be accountable generally for these different data points. We know that CRG has been in the game for a long time as a designated agency. We know that the DRJS also has certain functions that include things like rulemaking or setting recommendations for data governance and things like that, and for also aggregating data. One of the things that we didn't mention in our prior presentation, but that I find it really important to mention when we talk about our work is that the DRJS certainly, yes, is focused on criminal legal data because that is our mandate. And yet, because we're housed in the Office of Racial Equity, we also choose to regularly acknowledge that there are so many upstream and wraparound factors contributing to the pipeline that funnels people, usually disproportionately people of color, into the criminal legal system, that for our division, it's really important that we also look at systems that may exist technically outside of the criminal legal landscape, but that are real drivers, right? We can't talk about recidivism without talking about housing instability. We can't talk about the school to prison pipeline without talking about zoning and segregation and the impact that that has on what schools people are even going to. So thinking about all of the ways in which systems are impacting people and funneling them through the system means that there may be data points that are relevant that somebody is or should be collecting. And we wanna understand how all of those pieces are fitting together and who's on point for keeping those data and telling the story with them. So I get the sense that my remarks may have been a little bit scattered, but I hope you generally understood what our stance is on the bill. I would invite Andre or Laura to fill in or correct anything that I may have missed. And I welcome any other questions the committee may have. Thank you.

[Rep. Martin LaLonde (Chair)]: Over to Andre O'Gouraud.

[Andre O’Gouraud (Division of Racial Justice Statistics)]: Yes. Thank you. And for the record, I'm Andre O'Gouraud. One thing I wanted to talk to you more specifically is a question of data lake. I think it's it's a concept that in theory is very appealing because we all are striving to have a consolidated, centralized data access that we can that is ready for analysis. I think it's extremely important to understand the steps that exist between data collection in its raw form and data storage in a form that is ready for analysis. Daylight is really, its aim is to bridge those this gap between collection and getting it ready for analysis. My concern is that this gap is not it might not be properly addressed. Right? In that, there needs to be a clear understanding of how the data is is collected, especially if it is to include historical data where formats may have changed, vendors may have changed, and there's a lot of like, it's echoing what I said earlier. There's a lot of work that needs to go into understanding what the data are, their formats, how they need to be cleaned before they can be stored in such a way that they can be analyzed by various parties. And so, again, the the main concern I want I want to express is that those steps, I think, need to be made explicit so that we understand who is responsible for getting the data in shape so they can be ready for analysis. And that brings me to my second point, which is who do we put place a burden on for this work? I know that a lot of the agencies may not have the dedicated staff able to prepare the data in such a way that it can be transferred directly from the agency to a data lake that's made by ADS, for example. And I think that being able to lean on the expertise of different people to make sure that the data are are prepared in in the best way possible and to create consistency is important. So to echo what Susana just said about making sure there is not too much overlap or there is a clear definition of responsibilities in terms of who touches the data, who is responsible for cleaning, who is responsible for uploading, so on and so forth, is extremely important. Without volunteering any any service, want to emphasize that part of our division's role is exactly that. We bring expertise in understanding this data. The work that Laura presented speaks to that, and we are also mandated to do a lot of this analysis ourselves. And so, yes, I just want to share this perspective that there's those those gaps in the process of going from collection all the way to analysis that need to be properly addressed if we don't want to create redundancies and also potential gaps in the data that render it really unusable without doing additional work that then falls maybe on people that already have a limited capacity to do that.

[Rep. Martin LaLonde (Chair)]: I really appreciate that. You for that, Andre. One of the issues that I think we face with respect to data is we talk about the gathering of the data and the storage as you call it. One of the other gaps that I find that at least in the work we do is understanding what the data is telling us. What are we to do with the data that we've gathered and how is it going to answer questions that go into policy? And that's one kind of ongoing struggle that I've had at least. And there are some data that, I'll use bail data, for example, being able to track the trends with respect to bail relative to some of the policies that we're enacting on bail, for instance, is helpful. So that's one I can understand, but it's kind of interesting. And we have some different questions than what we've asked the division of racial justice statistics to really evaluate. But I mean, specifically with respect to the division, we're hoping to uncover and understand disparities in our system. We have other questions that we are trying to answer. But anyway, enough of that. If Laura has any input as well, or

[Laura (Division of Racial Justice Statistics staff)]: No, I just want to echo what both Andre and Susana said, that I think that those are all important things to keep in mind. And just to reemphasize what we brought up in our earlier presentation of infrastructure and relationships And resource allocation for staff to do work.

[Rep. Martin LaLonde (Chair)]: Who's that as well? Yes. Data is not free, I'm finding. So, all right. So Martin. Yeah, go ahead. Yeah, go ahead, coach.

[Rep. Kevin “Coach” Christie (Ranking Member)]: One of the other things that, you know, we found out over over time, you know, is is that with some of the entities, there there are memos of understanding that are necessary as well. How how has that been going, for the bureau?

[Susana Davis (Director, Office of Racial Equity)]: Well, so thank you, for the question, representative. It we're still early in that process, but so far, no problems. We recently completed or in the process of completing one with the Department of Corrections, which I think is gonna represent a significant chunk of the information that we're interested in and looking at. I see that as a positive step forward. But again, part of it is, again, looking at who's got which data. And if the judiciary has some data on bail and DOC has some data on bail, knowing which agencies to go to for what is a continual effort, a continual line of inquiry.

[Rep. Kevin “Coach” Christie (Ranking Member)]: Great. Thanks a lot, Simon. Because, you know, I I think about situations, you know, that we hear, at in our work, as policymakers and their real life stories, You know, that's that's the other piece. When you when you take that data and you take who is that person or who is that that chunk of information, it starts to answer some of the questions that Martin is asking as to how we can affect change along the stream, so to speak. You know? Because I, you know, I can think of, you know, situations where a student is having a hard time ends up becoming part of the system as a result of truancy, you know, and getting into trouble. And the next thing you know, they're in the bail system. They're in the correction system, and their whole life is changed. And it it as you start to disaggregate that data or that story, what you start to find are those things you talked about earlier. Housing might have affected it. You know, they might have been living out of their car and trying to go to school, you know, those types of things. And and all of those things have an effect on that on that story of that piece of data. So, anyways, if that helps, thanks a lot for all you're doing, my friend.

[Rep. Martin LaLonde (Chair)]: Yep. So thank you. And, Andre, if you want to comment on that, go over to you.

[Andre O’Gouraud (Division of Racial Justice Statistics)]: Yeah. I want to first say that I deeply appreciate the intent of that bill and doing exactly what we're talking about, which is facilitating data sharing and centralizing data in such a way that is more easily accessible. I think that's critical to the kind of work we do. Kind of going back to that question of knowing what those data are good for, right, what they can tell us, what is the purpose of collecting all these data and putting them together. I think that's part of our mandate at the at the RJS is that we have the ability to really kind of shape and without getting true technical on what we call metadata. Right? This is something that came up in our conversation with folks with at, AHS, which is that, really kind of creating best practices for agencies to attach this metadata to the data that tells us what does this data actually tell us. What is what are the the the functions of the data? What do they mean? And so this is a good example of what I mean when we say we think and we can lean on our expertise in understanding what the data are to help or guide this process and make it more standardized across agencies, make it more predictable so that we we have sets of best practices right away that can be carried forward in the future and really will lessen the burden as they get shared more widely.

[Rep. Martin LaLonde (Chair)]: Appreciate that. To

[Laura (Division of Racial Justice Statistics staff)]: the point of the MOU piece, that's part of phase two of this gap analysis project is understanding what agencies and departments have MOUs with each other so we know where the exchange of information is coming in as well.

[Rep. Martin LaLonde (Chair)]: All right, so we'll go to Pat. If you could, Patrick, you join us? I'm joining.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: And I'm going to try to join the Zoom again. I didn't realize you wouldn't necessarily have the slides I prepared right in front of you, so I'm going to try to share them on my

[Rep. Martin LaLonde (Chair)]: We can bring them up on

[Andre O’Gouraud (Division of Racial Justice Statistics)]: our PC.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: Yeah. Okay. That'd good. We'll see. I can I can speak to them if necessary, but we'll see what happens?

[Rep. Martin LaLonde (Chair)]: I should've I think I just got it up on my iPad

[Nate (Committee staff/IT host)]: Go for it.

[Rep. Martin LaLonde (Chair)]: It should be. Wait. Have a technical wizard in our midst. Thank you, Nate. Wonderful. Thank you. Yep. I'm not I'm not gonna do that.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: Yeah. We can you can zip to page two here. And, yeah, I'm just gonna briefly talk about who I am and my background with Vermont traffic stop data. I am Patrick Attilia. I live in Queechee, Vermont, and I've been and and then I'm gonna talk about the you know, I think what the racial officer racial equity is doing and the division of racial justice statistics is right on. I support what they're doing, and I think they're going in all the right direction. So what I'd like to do, provide a little more motivation and rationale for kind of pushing from the legislation side as, you know, why I think this this bill is important to support and, you what what they're trying to do in GS. So, yeah. So if we go to the next one about who am I? Yeah. I lived down at Peachtree. I know Coach well. Hi, Coach. I have spent thirty five years in a technical field as a software engineer and a manager. And specifically to this purpose current purpose, I did a lot of data work, data warehousing, database analysis, produced presentations, and synthesis for executives in my past life. Now I have no day job except for this kind of thing. Use my technical expertise to try to do good things. Yes. So specifically, I joined the racial justice alliance data team. Initially, I was the racial justice alliance data team in 2019. And I asked asked Mark Hughes, you know, how I could contribute in Vermont. And it turned out that there was this pool of traffic stop data that was out there. And so I I dug into that in 2019 and produced some analysis of the data that had been gathered up to that point. And I actually presented in the state house our findings there at the 2019. At that point, I got at the end of that year, I got became aware of what doctor Stephanie Seguino from UVM and Nancy Brooks from Cornell had been doing. They did the driving while black and brown in Vermont paper. And, so I linked up with them. And since that time, since 2020, really, I've been working with those two and continuing to continuing on an annual basis to pull down the data. And I do the I do the techy part, and then the three of us work together to analyze that and publish significant findings. And we're currently doing that process for 2024 data that became available last fall. Okay. So slide four. So, yeah, some some motivation here. First of all yeah. So what what has the, traffic stop data legislation, which that first came to being 2034 and has been, amended and improved over the years, the legislation, which has some follow on legislation. What has this done for us? First of all, it's made the, public and law enforcement legislators aware that there actually are, racial disparities in our law enforcement agencies. And the data you know, the we have the data for all law enforcement agencies in Vermont. Initially, it was very fragmented, and it took a lot of work to pull it all together. I'd like to point out that at this point, I may be the only person that has all the data from all the years in one place aggregated into one format, which was quite an effort in something that's been going on for some years. So we can now see the trends and what how things are changing over time. But just to provide that awareness and the accessibility of that data, And anyone who can hear me, know, they're welcome to have access to that data as well. Happy to provide it. Certain agencies within Vermont have responded to that this data set and doing good things, particularly the Vermont state police. They've they've changed some of their policies and the procedures, and, that's been a positive result. And we're seeing fewer traffic stops, fewer unnecessary traffic stops, a reduction in some of the disparities. More can be done. But we have that training in media. The data, you know, allows them to work within their own agencies so it doesn't have to to the public. Go ahead.

[Rep. Martin LaLonde (Chair)]: There's something you said, a kind of sparked the quest here. Go ahead. In my mind, you said there's less unnecessary traffic stop.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: There's a Like, I guess, just kind of a So so there's there's two kinds of of you can you can break traffic stops down into two pieces. One is what we call safety stops, where that's, you know, where speeding and DUI things are. And the other is things such as expired registration or, you know, license plate, things like that, or broken tail light. Right? So that's historically been a source for law enforcement to just have a pretext. It's called pretextual style to to with other motivations, let's say. Those tend to be not as necessary, so you find, and they tend to be ripe for abuse. So it's a pretextual traffic stops that I was referring to when I said I know

[Rep. Martin LaLonde (Chair)]: with the registrations, we don't have any indication anymore of license plates. So I'm sure those are plummeted. Yeah. That would but not the stark argument. But I would push back on something like a broken taillight

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: Uh-huh.

[Rep. Martin LaLonde (Chair)]: Being a safety issue.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: It it is it can be a safety issue. Yes. But it's not on the same level as speeding or erratic driving. Sure. I I tried to to draw that distinction.

[Rep. Karen Dolan (Member)]: It's Yeah.

[Rep. Martin LaLonde (Chair)]: Okay. So you had a line and had that break in somewhere. Yeah. Okay.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: And then the the state's attorney in Chittenden has also ruled created a rule against pretextual stops that I mentioned. So in and of themselves, it could could be a secondary reason for for ticketing someone. But I've forgotten her name. But anyway Sarah George. Sarah George. And there's also been legislation in past sessions about reducing contextual stops.

[Nate (Committee staff/IT host)]: Thank you for the clarification. I'll pass in

[Rep. Martin LaLonde (Chair)]: quick. Yeah.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: So moving along. Let's see here. Yeah. We've we've seen a decline in racial disparities. It varies by agency, but and we have to believe that's one been one of the benefits. And communities have been empowered to address biased policing because they have the data. So over the years, we've been called up called upon from various towns to doctor Seguino and myself to do presentations to the data because when the results have been posted, you know, people are concerned. So we've been happy to help with that process. Alright. Three out of five, please. So the way we, I think about what to focus on and to prioritize, there are a lot of data points out there to even within the scope of the criminal justice system. We focus on the two two criteria. One, we call our high impact decision points.

[Nate (Committee staff/IT host)]: Great fall.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: And then high discretion decision points. Now, high impact decision point is something that happens during that whole and we think it's a soup to nuts with a a first encounter with a police officer a last encounter with a parole officer potentially, all through that. And there's many decision points that happen along the way. And some of those think about pretrial detention, for example. Well, can affect an individual's ability to support their family if they're detained, can affect dramatically affect the employment status and prospects for the future, their standing in the community, their freedom, their privacy, even their lives. So there there are you can identify these high impact decision points along the way, and it provides a way to focus on what's important. And at the same time, many many of these decision points are high discretion in that someone has great latitude to decide one way or the other and potentially little oversight or accountability. So and then there might not be true. The transparency, the reviewer oversight, it may not be systematically recorded or centrally centrally recorded for possible review. And if it is recorded, it may not be collecting the demographic information that we'd like to have. Race, ethnicity, gender. All of those can be If they're not tracked, that can be a point of view. So the next slide, six, is a picture. I can't see it very well from back there. I'll wager. But all it is is, as I mentioned that, all flow through the through the criminal justice system process. This is specific to the adult adult criminal justice process. I didn't do one could do one for the juvenile justice system. And, the various steps along the way, from the first encounter with the police, arraignment, pretrial, trial, sentencing, incarceration, return to community. Each one of those, you can identify what those high impact, high discretion decision points. And this is just some examples if you it's an eye chart. Some examples of those that I together, I I put this chart with some quite a few resources. I talked to Bobby Sand and some others to try to put this together over time. Yeah. These are examples. So if we flip now to slide seven, we start talking about H3A2 specifically. And I imprecisely but attempted to circle the high impact, high discretion decision points that seem to be, fall within the purview of h three d two. You can see there's some many many are covered there. I'm quite happy with that. There are there are those that are not. And, again, these are just examples of this kind of decision point that could expand on this chart and do the juvenile chart and do the same thing. The priorities so so this is a good way to prioritize and keep focused on where we should be interested in in trying to bring forth this data. The I I think it's been noted here today that h three eighty two, the the areas that are are identified there are also have been put forward in the past by the RDAP. There's also the racial justice statistic advisory council, has also produced a set of priorities that's consistent with three eighty two. And so this is kind of my framework. Number eight. So I talked about the benefits we've seen from the traffic stop, David. Great. What?

[Rep. Karen Dolan (Member)]: Yeah.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: Thank you, Nat. Nate Nate, you've really done it for me. Yeah. So zooming out and looking at the whole this whole framework, I just want and, again, the the division of just racial justice statistics is heading in the right direction, and I'm just trying to motivate here. So the first first thing, a benefit is of this legislation would give, I hope, the the DRJS more boom. For more statutory and policy support for for their work so they can point to that when they're working with the other divisions. Say, point to statutes. Yeah, this is this is important, and this is a priority. And, of course, we're trying to reduce disparities in the criminal and juvenile justice system. We're trying to protect protect civil rights here. We live in a time when due process and equal protection are are under threat, and we're you know, we would hope that this would support our civil rights. And we've also heard earlier accountability changes behavior. Sunlight's the best antiseptic. There's something called the audit effect. You can look it up in the financial industry where behaviors change within businesses when they know they're gonna be audited. So not that there's necessarily any ill intent there, but just having, having the scrutiny or knowledge of the scrutiny will, will you know, it changes behavior, and builds trust and confidence in the data. Early detection of systemic problems. So it's very inefficient and harmful to to have a problem within a system not come to light for years until it becomes very bad, And then you need federal consent decrees. Better within the organization to say, Okay, this is an issue. We didn't know this was happening. Let's fix it before it becomes a problem. Early detection. And Martin said it before. This will help policy. Having this dataset over time will help produce good policy to try to forestall unintended consequences to build on evidence for lawmaking policymaking rule. Restoring and building public trust is always worthwhile. Empowering defendants and the defense counsel counsel in certain cases when when they identify, they can look at this data and see there's a systemic problem here. There's systemic bias, and this data could be useful for them. So that alright. That's the the crux of what I wanted to say. If we go to slide nine I just wanted to point out that we would Vermont is not, you know, out way out in front here. Particularly, you you all they all know all of this. But New York New York State has done great work here. There's they produced legislation in 2019, and they've got a portal. You can go see it. It's got tons of data about pretrial determinations, arrest disposition, sentencing. They've got a dashboard that is pretty awesome. And probation, recidivism. And yeah. And there's quite a bit of documentation or reporting about the benefits that New York State has been seeing already since 2019 having this data systematized. And as other states are are are are doing some things as well. California is up there, but New York is really stands out. And, you know, it's opportunity, I think, to collaborate there and and learn from their best practices also. Yeah. Slide 10 just shows Later, you can lose some of the things that are available on that New York State system. And you can open up any of those and visit all the data, and it's like gigabytes of it. Alright. To summarize slide 11, please. Yeah. Questions now, of course, or always contact me, Diversified PAT. Yes, I recommend you support and give favorable recommendation to 382. And also support the Division of Racial Justice Statistics that focus on high impact, high discretion decision points. And yes, what we heard earlier, GALI, proving our data collection system is fundamental. Thank you. Questions?

[Rep. Martin LaLonde (Chair)]: That was very thorough. So I really, really appreciate those charts as well, particularly the decision points. I think that that's going be helpful as we go forward. So, yeah, Karen, go ahead.

[Rep. Karen Dolan (Member)]: Yes, and thank you for this. I feel like this really helps set the stage of the kind of places that we want to be looking at for collecting data. This is more of a curiosity thing, because part of what I'm looking at with data collection is making sure we have a sustainable solution to, like, do this going forward. And so I'm curious if you can clarify your role again. So were you a consultant to the racial health statistics program? So I'm just seeing that you have asked questions, and I'm like, wait. So did we ask you questions? Are you a consultant? Or what is your role?

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: Yeah. I'm I'm just I'm just a civilian who volunteers in various roles. And one of my volunteer roles has been working with the Racial Justice Alliance now for for six years. So we meet weekly. I'm retired. I just it's something I do. Okay. And and it's the same with the traffic stop date analysis. Every December, I kinda crank it up, crank up to my memory and and pull down the data. But it's it's a volunteer thing. So

[Rep. Karen Dolan (Member)]: But if we were to look at something that was more embedded or sustainable, sounds like you would support that as well. You're doing this because

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: it's Absolutely. Yeah. Absolutely. Yeah. I mean, the ORE is doing the the work within the state to make it to to implement a sustainable solution. This is not a but and I don't think that the the justice alliances the racial justice alliance is gonna go anywhere. Look. We, you know, we we kind of in parallel try to find find ways to support and promote fields of priority shoot.

[Rep. Karen Dolan (Member)]: Thank you. I'm just trying to get it back connected.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: Yep. I'm just a guy.

[Rep. Martin LaLonde (Chair)]: Oh, shit. No. Okay. I

[Unidentified Committee Member (former law enforcement background)]: appreciate the data collection. I think it's lacking enormously in one aspect if you are trying to look at where this stuff takes you and drawing conclusions if you're not part of the decision making process that involve the car stock.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: What what sort of decision making process you have?

[Unidentified Committee Member (former law enforcement background)]: We're dealing with police car that stopped the car and understand the decision making process that created the car.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: That's fair. That's fair. And it's a question that we just have to ask when we see the disparities coming, the racial disparities and other It's a question we have to ask, and it it it falls within the organization to really remedy

[Unidentified Committee Member (former law enforcement background)]: I understand. But I mean, could some people be more prone to traffic violations than others? I don't know if they are in other crimes. Absolutely. So I think unless you're part of the decision making

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: part We're talking about some some individuals or some, racial categories?

[Unidentified Committee Member (former law enforcement background)]: Well, if you break it down, you know, in the in the yeah. For all kinds of things, be it socioeconomic background, any all sorts of things. But if you weren't involved in the actual decision to stop that car, you've missed a large data point.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: But was there is there I guess fine. Is there any additional data that you that would would it's something you can tackle from a data standpoint?

[Unidentified Committee Member (former law enforcement background)]: What was I thinking? And what what was the reason that I decided to stop the gun?

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: I mean, can you capture that? Probably

[Unidentified Committee Member (former law enforcement background)]: not. Not unless you're riding in a car with the police officer.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: So I, I guess, an individual stop, that's true. And I I understand what you're saying. It's just when you start to look at large numbers, and I you know, we've got 1,300,000 traffic stops that have been recorded over the when you start to look at demographic categories, calls into question. I understand what you're saying.

[Unidentified Committee Member (former law enforcement background)]: I've swapped thousands of cars.

[Rep. Martin LaLonde (Chair)]: Mhmm.

[Patrick Attilia (Volunteer Data Analyst, Racial Justice Alliance)]: Do you find that there's more criminality among certain types of people?

[Unidentified Committee Member (former law enforcement background)]: Not necessarily.

[Rep. Karen Dolan (Member)]: Okay.

[Rep. Martin LaLonde (Chair)]: So anyway, the data itself is required through legislation. And this is you're evaluating the data that we have required in our fair and partial policing policy and law. So I think that that's important to note, that we have made some strides in that one area to provide, to gather the data, but there's other areas that I think three eighty two are kind of highlighting that we need to continue to work on. And that is, I think, this really introduction to the work that we're going to need to be doing over the next biennium and beyond. So really appreciate your testimony. That's very helpful. Although I see Susana has her hand raised. I'll give Susana then Coach the last word, and then we'll be transitioning to H410 as soon as we get Michelle over here.

[Susana Davis (Director, Office of Racial Equity)]: Thank you, Mr. Chair. I apologize for jumping back in again after our time elapsed, but I couldn't help myself. I think that the last portion of this conversation was really important, and I just wanted to lift up the fact that this is why qualitative data are also so key in all of this work, because there are a lot of aspects to this that may not necessarily be easily capturable through quantitative data, and it doesn't mean that they're any less important or present. And I mean that, yes, on behalf of communities, but also on behalf of the communities as well, right? We're just made up of people. And so, it's a big priority for us to be able to do our work accurately and with fidelity when it comes to the quantitative, but we don't wanna lose sight of the qualitative information because that's what's helping us to tell that story again with the data. Thank you.

[Rep. Martin LaLonde (Chair)]: Great. Appreciate it. Thank you. Coach, any last word? You're, muted.

[Rep. Kevin “Coach” Christie (Ranking Member)]: There we go. I'd I'd like to thank everyone and especially Pat, and, the team from racial justice, you know, alliance. You know? Because when you think about it again, you know, he put another time stamp on that going back to 2014. You know? And and we've been working on this even then. So just, you know, the snippet that I bought up was a ten year window. You know? Now we're looking at close to fifteen years. You know? So, it's it's just fascinating, you know, how deep that lake is. So I'll I'll I'll leave us in the water.

[Rep. Martin LaLonde (Chair)]: Alright. I appreciate that. Thank you everybody. And we'll waiting on Michelle to come over for a walk of h four ten. So we'll go walk fly recess, I guess you can call

[Nate (Committee staff/IT host)]: it.