who are victims of violent crime they can apply for a u-visa. and we file more of those than anybody at least in the state the vast majority are not granted, unfortunately, even under prior administrations, but we have a robust policy of making sure that people are aware of that right and facilitating the application the other effort we've taken is training all of our staff on the potential for i.c.e. showing up in the court houses, as has happened in the country, both texas and pasadena, california, to make sure that we're aware what we can and cannot do should i.c.e. show up looking for victims, witnesses or defendants, to make sure we're a good partner in the city family. so we've trained our victim advocates to be that support for anybody that comes to the courthouse, expressing fears or concerns about immigration consequences within the building, but we offer an escort

to that individual throughout the court process to make sure that they're protected and our advocates have the hotline information so they can call immediately to get immigration counsel for anybody confronted by i.c.e. in the courthouses. >> thank you. >> any other comments or questions? thank you. >> thank you. >> next, i would like to invite up, dr. bennett, with the department of public health. thank you for being here. >> i'm dr. bennett, director of interdivisional initiatives, which means that i do work that crosses from environmental health and health education and those things and the san francisco health network, which has our hospitals and clinics and behavioral health come poep

-- component, so i deal with the data on both sides, which is how i get to be here. so the department uses data pretty extensively. we have very little choice about having the data. most of the time large amounts of data are required almost us for state, federal or grant-related reporting. and the medical records that are used for clinical care. so we have lots of data. and we also use data for program planning, for billing, for services, but most importantly, we use data to improve program quality and service delivery and health quality. so we do that by tracking outcomes. we do it for directing programming to populations most at need or patients most at need. and for evaluating and setting policy. so i'm going to give just a few examples, because i was told to

keep it short. before i even do that, i will direct people -- most of the data -- not most, but a large amount of the data the health department collects, at least that that is not specific to patients, is publicly available. probably the easiest and most user-friendly source is sfhip.com, the collaborative of all the hospitals in san francisco, which is overseen by the health department and gathers data from across the city and we consider things like educational attainment and unemployment and economic health data. so that's number of strokes and visits and things you think of. so sfhip.org. so the example of how we track outcomes, i will take from the san francisco health network, primary care clinics that

include castro, mission, and places you are familiar with. so these slides are hard to read. i think it's the screen. what we see on the slide is the clinics and this is hypertension control rate for black african american patients in each clinic. of all the people, they know who has hypertension in the clinic who are african-american, which of them have that hypertension under control under that medical standard and have set goals for themselves about what level all the patients should be at and the goal next to that is that there should not be a disparity between groups. the largest disparity is between black african-american patients and the average and that's what all the clinics are focused on right now. some are quite small.

so we know our patient population is really concentrated. in some clinics, it may be just a few people, which is why that line looks so busy. month to month, the clinic judges themselves to get an idea of how you patients are. next slide. for directing programming, i will use our getting to zero program that's care of h.i.v., and prevention for people at risk for h.i.v., very heavily focused on in our clinic sites. the main thrust is out of the research and health education departments or areas within the population health division. th this slide shows what has happened with h.i.v. it's a national model. if you look at the green line,

it's dropped dramatically over the last 10 years, as have deaths and new infections. people living with h.i.v. is a good thing because it's mainly because that's where we got that. this is a slide of just women. that blue line that is above everyone else, african-american women. so that lets the team know that while they have many reasons to feel good, there is work still to be done and that's allowed them to create new projects around prevention. if i used other slides, i could explain why they're focused on transwomen and on youth. all of those are groups that are not following that trend that the general population of

h.i.v.-positive people or people at risk of h.i.v. are following. so that's how we use that data to create equity, we look for data that looks positive, you look to see if it's positive equally across the population. and setting policy. this is out of the office of inclusion and work force development looks at what the racial and ethnic makeup of our work force is and this compares that to the makeup of our san francisco population, which is our client base. so white, asian, african-american, if you go down, you can see the different groups and that's where the wide gaps are. first, more white or caucasian staff than population in the city. so the red bar versus the blue.

we have fewer african-american staff than we have people in the city. it's looking at that imbalance and helping to set policy about how we oversee hiring panels, what kind of oversight we do in terms of recruitment, hiring a recruiter to improve the racial diversity of our pools for applicants, all of those things were done in response to looking at that data. next slide. and then just an overview of what our approach to data is. we use two methodologies. one is lean, coming out of industry that looks at how to improve a process. and when is results-based accountability that's out of public government, looking at how to improve services. and it basically says, use the data to define the problem, so collect good data, analyze the cause very deeply of why your data looks the way it is.

make changes. measure the impact. and then make changes again and repeat and repeat and repeat. so data is meant to be part of an iterative process that says, we looked at the data. we made decisions based on the data and keep going back again and again, so the data is an ongoing endeavor. not something we do one time it. has to be something that you can look at in realtime in order to be useful for all of the uses. questions? >> thank you. this is interesting. i know you are giving us snapshots of different things going on, different data that you are collecting and analyzing, but tracking outcomes. do you track them across the board for just about everything or do you choose strategic things to track?

there are things about which we have no choice, like hep a outbreak. other things are mandated but with choice. so we have outcomes around our value-based payments. so part of the way the system is converting to reward quality work in medicaid and medicare is by paying people based on their outcomes. which outcomes they pay for is somewhat mandated by the federal government and somewhat up to the institution. we have chosen some like african-americ african-american/hypertension. we were required to do an equity measure and we chose that one. and others are based on what we see in the population. h.i.v. is a very salient issue in san francisco. it might not be something tracked as closely in other jurisdiction, probably to their

detriment. and other things are clinic-specific. so every entity within our system is doing a slightly different look at their own data and people have some agency around having their own data and each clinic can look at what they, themselves, are doing and then when have both mandated and centrally decided measures that we use. we do have measures that are used across the department to judge quality of work. >> is tracking live births in the city data that you collect? >> yes. that is state mandated data that we and every other medical unit in the city send. and mortality data. hospital admission data. those things are on sfhip site. >> very interesting. look forward to having you back to go deeper into this. any questions or comments? we'll move open to the next

speaker. >> i will slink away, too, because i have to go parent. >> thank you for being here tonight. next i would like to ask our public defender to join us tonight. >> thank you. i think what i heard the district attorney say is that the system is not racist. i would disagree with that. everything was explained by other factors. and while i think that's partially true, i think that the data definitely shows that the system is not race-neutral. i don't think there could be any dispute about that.

even though our african-american population is 5%, 55% of our clients, public differ ender clients, and folks in the system are african-american men and women. if you look at categories of crime starting with traffic tickets, african-americans are seven times as likely to be stopped for a traffic violation. why is that, because african-americans can't drive? more likely to be stopped for a drug offense, even though every study has shown that whites use and abuse and sell drugs more. if you look at the overdose rate, six times the rate for whites than blacks, but the statistic is the opposite when it comes to being arrested for drug offenses. james bell, who spoke earlier, did an amazing, groundbreaking

study. it showed clearly that there were disparities. we sought to take that study and work with the university of pennsylvania, which offered to do a two-year study independently. all we did was provide them with our files. and we opened our files up to them. they came up with a report -- first slide, please? second slide. they gave us an economist and a scientist and a law professor for two years, who worked on the study. and we gave them 11,000 cases. when asking the question, "what specifically did the disparities result in?" they found that african-americans are held in pretrial custody 62% longer than

their white counterparts and on average serve 30 days longer in custody. these are folks that are charged with the same crime, same criminal history and all of the characteristics are exactly the same. so in other words, making an apples-to-apples comparison. next slide, please. in terms of cases, the time to resolve a case is 14% longer if you are african-american. and on average, it was 90 days to process a case of a black defendant. 77 days to process a case for a white defendant. same charges. same criminal history. same background. defendants of color are convicted of more serious crimes than their white counterparts. 60% more felony charges and 10% fewer misdemeanor charges.

next slide. defendants of color receive longer sentences than white defendants. sentences received by blacks are 28% longer than those received by whites. probation sentences received by latinos are 55% longer than those received by whites. next slide. people of color receive more serious charges at the initial booking stage and the reason why this was significant, again, you ask the question, why, why do we get different out comes? for a person of color for the same offense, you will be charged with more serious charges and more charges. and that's when the police determine what the charges are. and they found that that disparity starts with the police charging and goes throughout the system. in other words, the district attorneys, defense attorneys, are responding to the charge. so you might have a black

defendant that will be charged with more serious charges than a white department for this same conduct, given the same criminal history. so it has a ripple effect that effects subsequent charges and results in a person of color being convicted of more charges than their white counterparts. so that gives you an overview as to what the study found and we have a link to the study, which is quite comprehensive. the question is, what do we do about it? how do we address this? you will also see that there's a chart at the end of the materials that you have and i have them for the audience as well. that's a breakdown of exactly what we do in our office. we're able to see for every teryn now how many pleas, trials, misdemeanor pleas,

dismissals, diversions. and it's allowed us to be able to look at exactly what our lawyers are doing. we're interested in the disparities in the way that our lawyers are representing clients. and we did find disparities we found one attorney that pled clients guilty to 30 felonies in a year. we've had another that only had 5. so we started to look at those and say, which lawyer would you want? somebody that fled somebody to 30 felony pleas or somebody that pled 5? we started to look at the practices and we saw differences in outcomes because of the way that cases were being handled. so we have implicit bias training and mandatory coaches and meetings to ensure that we're addressing that within our own office. you also see the stats that we

published through our annual report and we have an annual report -- it's against the law in san francisco to use city funds to print an annual report. so i pay for this myself, but we print 5,000 of these and we have all the statistics related to every program in our office. that's how we track our criminal justice outcomes. to answer what to do, the first thing we have to do is we have to disrupt. and, for example, you see that one of the main culprits is the bail system. when a high bail is set and you can't afford to pay, you're in jail and the only way that you get out of jail is you plead guilty and a lot of poor people do that and people of color do that. it means you are on probation. you give up your right to a jury trial and ultimately, you will be in the system. and so what we've done is we have now filed bail motions in

every case. in one weekend, we filed 800 bail motions and it almost shut down the courts. why did we do that? court were unconstitutionally setting bails that people can't afford. when a transgender activist was arrested for trumped-up charges for assault, she had a bail of $173,000, no prior record. fortunately, our early release unit was able to work with the d.a.'s office, provide them the information that showed she was innocent. 250 people showed up at her arraignment and the case was dismissed. otherwise, she would have had to post bail. even if the case was dismissed, she would have been out if she put up $17,300. that happened to a number of our clients. so we filed a lawsuit in federal court and state court and we have, i think, seven lawsuits

going on right now. again, disrupting. as a result of that lawsuit, the city attorney, and others issued statements that the bail system is unconstitutional. today we filed an appeal in state court and just today, about two hours ago, got a decision from the court of appeal saying that the way that san francisco courts set bail is unconstitutional. that's a huge way that we're disrupting. we're trying to work to change the law. sb 210 is working through the legislature. we're concerned about that because we think the judges are trying to hijack it and we wouldn't have real bail reform. so we're working on that. and the other thing we have to do is we have to repair. in san francisco, tens of thousands of people have been convicted of marijuana-related

crimes. they're now under prop 64 entitled to relief. who will make sure they get relief? we've had in the public defender's office a clean-slate program for 15 years. and we clear about 2,000 records a year. and we make sure that people bring expungements to court and we handle that process for them. very important to do that. another huge reform is fines and fees. and we're about to introduce a bill that would eliminate all the fines and fees collected by san francisco superior court. if you plead guilty to a felony, there are no less than 32 separate fines and fees that how of to pay. everything from courtroom maintenance to building new courthouses around the state. they have all these fees and they total $2,000 to $5,000. so we have legislation now, which we hope you will support, which is being supported by the supervisors, that's would eliminate almost half of the fines and fees, including

probation fees, which are $30 to $50 a month and automatically charged before a person is placed on probation. beyond that, i think that we have to do what you're doing. you have to question. you have to study. you have to look at this. i appreciate the presentation by the elected official from minnesota, home of prince. i think that we will, you know, be in a better place to look at those issues if we start from a place of reality. and the problems in the criminal justice system, as you know, are only a small part. we have it look at all of the factors that you are looking at, and that's why you are talking a big-picture approach and looking at employment, youth services, family services, foster care, and only by looking at all of these things and ultimately

addressing all of these things will we have true equity. thank you. >> thank you. colleagues, are there any questions now or can we move on to the next speaker? we look forward to having you back. next, we're inviting to the podium, our elected sheriff, vicki hennessy, san francisco sheriff's department.

>> good evening. i apologize, because i didn't get a chance to email our presentation. i put it on the overhead. there it is. okay. thank you. i'm here to answer the questions you asked us to answer. the question was -- why do you collect data. it looked pretty good. [laughter] >> can we get this on the overhead? it's not working?

>> i am here to answer the questions you asked us to answer. first of all, i want to thank you for having me here and i want to thank james bell and toni carter for their presentations. i jotted down a number of things that i'm thinking about as well that could help me in operating the jails. as sheriff, one of my main responsibilities is managing the county jails. now i'm not necessarily the person that puts people in jail, but when people get there, through arrests and stay in my jails, it's something that i'm concerned about and i want it ensure that we're doing what we can to help with the racial equity issue. what do we do? why do we collect data? to better understand who is in custody or sentenced in one of

our out-of-custody, alternative programs. 85% of the people are pretrial. 15% or less, sentenced to county jail. most of the people are out on pretrial alternatives. we collect name, age, address, booking number, case number, sf number. we have our own jail management system that somewhat interacts with court management system and somewhat interacts with the other systems in criminal justice and i will echo what christine deberry said. one of the things that i hope will come out of this is that we will gather, identify first, and gather excellent data, so we can make good decisions, and we're not there. and i think that was said by a number of the other people

before me. we're not there. i'm hopeful that the justice system will be able to come up and get together and there will be more investment in it, so we can get the data we need. and one of the things that i'm doing in my budget is my jail management system that collects all this data and more, but also interfaces with other systems, but not as much as we would like. we don't get regular reports that we would like. it's very, very complicated to get a report out of the system. i'm asking in my budget for a new system, which will be a lot more flexible and we'll be able to do a lot that we can't do now. so what -- for what purpose does the sheriff's department use the data? well, on the big -- in the big picture, we share the data to

better understand and evaluate who is in our custody or in our programs. last year -- actually, it was in 2016, we were very involved in the reenvisioning the jail with many community members at that and working on that and we were very frustrated at that time by the inability to capture data that we needed for that. we do this and we work with our criminal justice partners as much as possible, so for the reports that the d.a. talked about and james bell talked about, much of that came from our jail management system. how does the sheriff's department use the data to advance equity? in one way, we use it to identify the different -- the racial disparities at the difference decision making point in our jail. one of the things we're doing is doing research to gather data to inform future policy decisions

about what we learn through our research project that involves who gets out on alternative sentencing and who does not. as well as working with the pretrial diversion project, identify wooing gets out on pretrial and who doesn't and why not. and so those are things going on right now. the new pretrial system, risk assessment tool, has been going on since may, 2016. and it's shown a lot of improvement, but we still don't know how much. and over sustained time, so we'll look at that as well. so on the next page, i gave you the data collected. we're one of the few agencies, where we do collect race and ethnicity and in a robust fashion. we have it so many of the departments depend upon us and come to us to pull our data, for

example, the police department asked us to provide data to them for a report they were doing and asked us to provide that because we had that about people who had been arrested. and so, as you can see, there's robust race and ethnicity data. so that's pretty much what i came here to tell you tonight, but if you have questions, i'm happy to answer them. >> thank you, sheriff hennessy. it's -- although you didn't present something to us before, what you brought today is very helpful, i think, for us as well as for the people that are here with us at the hearing about these key questions that were presented to you. and this -- this hearing, this particular hearing, is partially in response to a request that former mayor lee gave to us before he passed away untimely

and left us. as a commission, as an agency, we've been looking at the question of disparity on many levels, including and specifically racial levels and he asked us to begin this review of racial disparities in that sense and we do have from the mayor's office a representative. so thank you for that and thank you for putting that together. >> okay. >> any questions or comments right now for the sheriff? look forward to having you back to go more deeply into -- >> thank you very much for the work you're doing. >> thank you for being here tonight. next we can go to the san francisco unified school district.

thank you so much. and the superintendent is also with us tonight. >> good evening, commissioners, and good evening, members of the audience. thanks for giving us this opportunity to present our data practices and our data use. so i will begin with the first slide. i want to give you the scope of the data that we collect within san francisco. it begins at the time of enrollment, where we collect all the demographics. as you can see, it increases to the point where we actually look at college and career, not just graduation, but also through college. so we track longitudinally throughout those years. when they're in san francisco, we measure the whole child. so we look at both academic,

behavioral, social, emotional and culture climate data for our schools and students. we want to use data as a flashlight and not a hammer. so we try to make sure that data is used for continuous improvement as, as i said, for a flashlight and not a hammer, but keeping in mind the equity imperative. we use data for evidence-based decision-making and definitely for accountability. so when i'm talking about accountability systems, you know that all accountability systems are designed to measure the district. and similarly we do measure the district.

the number one goal is equity. therefore, data has to measure equity. and the second goal is academic achievement, so you can see evidence-based decision making, whether it's right from the classroom level to the district level. it looks at evidence and data and lastly, accountability goal in san francisco. at this point, i will show you -- let's go a little deeper into the equity imperative. how do we look at equity? the first way we do so is by holding high expectations or equivalent outcomes for every school within the city. so even though you see variable performance of schools within the city, they all are held to the same standards. so when data is looked at, it's all at the same standards that we look at those schools and we look at the disparities that

exist. so high expectations are there for all. we also realize that inequity starts at the input level. san francisco unified is a district of school choice, where parents get to choose their schools. and as a result, right there at the onset of the input variables, you can see differences in student demographics. the segregation, concentration, poverty concentration and race concentration in certain schools versus others. more english language learners, more special ed population. so programs in some schools versus others. other than that, the other input variable that goes into the school is the teacher factor. so even teacher stability and experience is variable across schools. so equity has to begin right in the beginning.

so we begin by actually looking at these input factors and giving more support to the schools that have the higher challenges. now i will move on to evidence-based decision making. in order to really make good decisions, you have it look at data across time. you can't look at it at just one point in time. so in san francisco, we do look at data across time. across multiple measures, as i was telling you about the academic, the behavioral, and the culture climate measures, and across various groups. you can never look at any data point as one number. today if i stood here and told you my graduation rate for san francisco unified is 87%, that really says nothing, but the

real data lies when i break it down by sub groups. lastly, more than just presenting the data, the use of the data, is in the dialogue that goes behind the data. so as an example, we have shown you some conversations that we have with school communities, with principals, school leadership teams around the data. when you ask them, can you determine your school priorities? what are your outcomes, your targets, but for which groups? ask which group questions. going deeper into the dialogue and asking about the practices, linking the practices to the outcomes is, again, the richness of the data, the sense-making of the data, comes through the dialogue from the data.

yours is a glance or a glimpse at some reports that are available to the public. we do present right from board meetings and parent forums to definitely most of our data for our school community. so we have the multiple measures displayed through time. but what i want it assure you is the aggregation of the data. we also look at it by homelessness, public housing, as well as foster youth. we also disaggregate both as an intersection of race and poverty as an indicator. lastly, we want to talk about theory of action.

we believe that as we continue to build a professional capacity, to make decisions with data, to be empowered with data, we will see our student outcomes improve and outcomes at a higher level because there will be that deeper information to make decisions and take actions. so that's our theory of action. lastly, we do partner with others. we share our data. we love to listen and learn as we did in this forum from our partners and from other school district or other city partners. we've given you examples of when and how we partner with the community, as i mentioned, through the forums that we just held in november, with the cities and the measures built

into the children and our families document. we also have research practice partnerships where we have a longstanding partnership with stanford. we have currently 28 research projects that are in collaboration with stanford that have not -- for them, they get the field-based insights and when get the research to apply. okay? last but not least, with the state board and with other california districts, so we also have partnership with all the big, major california districts. and we share our data with them. thank you. >> thank you for being present. any questions right now, commissioners? any comments right now? thank you so much for being with us tonight. >> quick question. i know there's been an increase in anti-bullying efforts.

is there any way that our school district is tracking the bullying going on for a variety of reasons in the school system? >> yes. we have a youth risk behavior survey, which has specific questions around bullying and it's administered and we do report the results. even in our culture climate, surveys that are given to teachers, staff and students, we do track that. >> and it's internally? >> yes, correct. >> thank you. >> commissioner ellington? >> just because the superintendent is here, i would like to ask a quick question. some of these facts we know and we've seen over the past 20 or so years. can you talk a little bit about how this new data or facts

presented today inform some of the things you are looking to implement? >> good evening. thank you for having us here tonight. one of the things that i -- i think it's important for a superintendent to do is spend the first three, four months listening and learning, gathering data, present a report to the board on our findings, strengths of the district and initial impressions, and areas of growth. as you said, we know over time -- that's something we've presented when we look at the data over time. especially the data around african-american youth, the disparities we've seen, we've seen over time. what we're putting in place now is a program called pitch. it really looks at targeting our resources toward african-american youth, resources, professional development, raising the capacity of the adults that are in contact with african-american

youth and that's one of the things that we're putting in place now. we're looking at 20 schools, targeting the 20 schools. we have schools that are historically have underserved and then we have high gap schools. so there are schools that if you look at the central number, you would say 75% of the students are profish -- proficient. but then you look at the data, it's 90% of white and asian are proficient and 20% african-american students. so we're targeting in a laser-like way around the students that we know need us most. one of our core values in the district is equity and social justice. that means that the students that need more receive more. >> i know earlier you mentioned social, emotional and cultural

climate. can -- it's an open question, but when i walk into schools, particularly in bayview, there is a visible difference in general appearance and i would assume there's some correlation between that and academic outcomes with that. what are some of the instruments that you use to measure that? >> the four social-emotional indicators that we look at, mind-set, self-advocacy, social advantages. and, yes, we do see differences across the social-emotional indicators across schools and sometimes even within a school across lace and even across english-language learners, we

see a different. in culture climate, we look at a sense of belonging. that sense of belonging indicator is when i see most differences. >> and once again, what we're doing is looking at the schools that -- one of the data points that you will see in your presentation is that tier 1, 2, 3 schools, and we're targeting more resources to tier 3, and whether it's around socio-emotional efforts or working with the school communities to do just that. it's more resources going to the schools that need more. >> thank you, both. for the record, i want to make sure that everyone knows that dr. matthews is our superintendent. and thank you so much for being here tonight. and ms. khan chief of research

and planning. we look forward to working with you more in the future on all of the issues of equity. thank you. next, i would like to invite to the podium, deputy chief connolly from the san francisco police department. >> good evening, madam chair, director dave is, members of the public. i'm deputy chief for the professional standards and policing bureau, san francisco police department. going to give a brief overview of data collection current and what's on the horizon. it's important to address the questions put forth to the police department. the san francisco police department is collecting demographic data in compliance with 96a city code, became effective in january, 2016. the data is presented in

quarterly results and we have se several reports that have been generated. they're available on the website. this reporting mechanism was developed in response to a national dialogue about policing and community encounters. it's mandated for only san francisco police department and does not include other law enforcement agencies that have policing responsibilities in the city and county. when you look at comparative data, there is none. you have a snapchat of san francisco police department. sheriff department has a different mechanism. the first question -- why do you collect this data? what is the intended outcome? when we look at this data, it's to identify the gender and ethnicity of those that we come in contact with. and what is the purpose?

why are we having contact? number two, what do you use the data for or how do you use the data? it's primarily compiled to generate a report in compliance with city code 96a. the 96a legislation was initiated to mandate this reporting by supervisor cohen. supervisor cohen was a pioneer to look at this collection. this does not exist -- had not existed in san francisco and sporadically across the country in mandating the collection of specific data. kudos to supervisor cohen for creating that mechanism and that legislation. last question is, how do you use the data to advance equity? that's a difficult question. when you are looking at data, how do you use that data and what does that data mean? how do you create mechanisms and

training programs to advance equity across all demographics? primarily, it's to demonstrate our transparency. what are we doing and how do we do it? secondly, for accountability. who are we contacting and what does it mean? to be quite truthful, we can collect the data, but it's disengenuous to look at that and tell you what it means from an academic standpoint. police department has been in negotiations to bring on an academic entity to determine what this data is and we're very close to that. lastly, we use the data and, again, this conversation, there's a great amount of history, but in recent history it's only come up in the last four years, when we're talking about what are place contacts.

what does our contact picture look like? as we look at the data mechanisms, we've needed to train our people on what implicit bias and procedural justice means in today's world. and that data is helping us to look at who are the people impacted by our work and how do we train our people to understand what implicit bias is and how we move forward in creating an equitable environment in procedural justice. on the horizon -- i mentioned that 96a reporting has been around for the last couple of years. san francisco and los angeles created mechanisms in our reporting. we created a mobile app on smart phones ton collect t -- to collect the data. when the state decided they wanted to do the data collection, they met with us and los angeles. and they looked at, okay, what

is the method by which you are collecting this data? they liked it. they liked it a lot. they said, you are so good, you even beat l.a. then we didn't hear from them. and then they sent out surveys to all over the state and said, how do you collect data to all of these departments and what do you collect? and then we didn't hear from them again. the ripa board was formed. racial and identity act of 2015. they started to put that together creating a ripa board and identifying personnel and kamala harris appointed a number of people and it's a collaborative effort to try to put the racial and identity

programming together. one of the mandates of the legislation -- what are the mandates of the legislation on the horizon? it's to collect information on all stops made by officers and import the information to the department of justice. the bill requires that each state and local agency, police officers to report to the attorney general data on all stops as defined and conducted by the police officers and include that information including time, date, location and reason for the stop. the bill further requires the employees 1,000 or more police officers to issue its first report by april, 2019. why does that bring me to this point? in april, 2018, the state department of justice will roll out training on how to collect this data and submit it to the state for the top eight

agencies. we're talking about top agencies that we call wave one, los angeles police department, los angeles county sheriffs, california highway patrol, san francisco police officer -- we're number five in the state. riverside sheriff's office, san bernardino, and san diego police department. we just met with the state. in the ensuing time from the time we first met with them and to show them our models, we met and they showed us the data template that they will roll out. we knew there would be a lot of data collected. in san francisco under 96a, we

would collect 18 different attributes or information field. the state took ours, took los angeles's, married them together, and there will be 41 fields of data points collected. within the data points, there's subsequent opening up blocks of information about multiple identities in terms of nationalities and gender. if there's a perception that somebody wants to identify with multiple ethnicities or genders, that's captured in that way. so there are some challenges ahead of us in terms of what that looks like. when talking about the state system, there's a learning curve. the system we developed in san francisco is not the same. we'll have to learn that system. they have a computer-based, web-based application.

we need to build it out. so there will be some bumpy roads in the coming months, not just for san francisco, but for the top eight. and then as the progressive years roll out, smaller agencies will add on to this. we're like the information guinea pigs, if you will, the system, the collection, and then they will correct it. there will be corrective measures. one of the key components is, we have a quarterly reporting. and that data is ours. under the 953 bill, the data belongs to the state. we set it up and they generate an annual report. they do a comparative analysis and that's supposed to be issued in july, 2019. with that, i will entertain

questions. >> i just want to say that it's very exciting to hear that the department is close to getting a contract with an institution that can take this data and show specifically what it means and we know without that, there can be no understanding of what the department is doing and certainly no reform. can you give us a sense, is it months or -- >> i would say it's a matter of months. interestingly enough, the ripa board issued their first report january 1. it's on-line. one of the board members is dr. jennifer everhart. we had been in extensiove conversations. she's a rock star in her field.

trying to get her researchers, it's been a long discussion, but she did help us to formulate the parameters of moving forward. i think in the short term, it will be coming very soon. and it's an exciting time in terms of data collection and looking at a comparative picture between the top eight because it's not defined by location. it's all over the state. i think it will be an interesting conversation proving forward. i applaud this commission. i think it requires a much more robust discussion in terms of how our collaborative partners are collecting data. the terminology equities, brilliant. i think we can develop a brilliant metric. >> i think i speak for my colleagues and the agency and saying that we're very -- looking forward to be able to contribute to this work of

reform that is so necessary for the community and the department and i know you have been doing a lot of work and the police commission has been doing an immense amount of work over the last year and a half, two years, and i think it's now an opportunity for the human rights commission to step in and become a partner with that. >> absolutely. >> thank you. >> i want to thank you for your leadership and support in this effort. now that we're no longer participating in the joint terrorism task force, what are some of the protocols or procedures when federal agencies and particularly the f.b.i. asks for data? what data can the city give or not give -- >> which task force? >> joint terrorism task force. >> we're not in contact with them. >> now that we're not, when there's a federal agency or f.b.i. trying to get ahead of an immigrant or someone in the tenderloin, what is the process or protocol that the police

department is undertaking? >> we're not in contact with them about those matters at all. if there's a criminal matter that the f.b.i. is investigating, f.b.i. -- it's not their jurisdiction, but we continue to work with the f.b.i. on criminal cases. >> okay. >> in terms of our relationship with immigration, there is none. >> thank you. >> i want to thank you very much for the exciting description you have given us about what's coming down the pipeline. i just was kind of trying to wrap my head around when the data is collected. is this day-to-day policing. is it happening at the station where someone is interviewing and reporting a crime? i wanted to see when -- at what point this data was coming