Amicus Presents: The Pre-Crime Unit

Summary

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Amicus Presents: The Pre-Crime Unit

Podcast: Amicus With Dahlia Lithwick
Featured Podcast: Hi-Phi Nation (Host: Barry Lam)
Date: February 9, 2019
Episode Focus: The rise of algorithmic, predictive policing in the LAPD, its philosophical implications, and community pushback.

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Overview

This episode, curated by Amicus, presents the first installment from Slate’s “Hi-Phi Nation,” hosted by Barry Lam. “The Pre-Crime Unit” probes the realities of predictive policing—law enforcement’s use of data and algorithms to forecast and prevent crime. Using Los Angeles as a case study, Lam explores the legal, societal, and ethical ramifications of this new technology, centering on community responses, how these tools work, and the profound questions they raise about fairness, privacy, and justice.

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Key Discussion Points & Insights

1. CSOC & Algorithmic Policing: Unfolding Debate in LA

• LAPD Board Meeting On CSOC Funding:
  • Police commission reviews a $35K grant for a Community Safety Operations Center (CSOC)—now LAPD's hub for predictive policing.
  • Community members, particularly the Stop LAPD Spying Coalition, object, citing surveillance and racial targeting.
  • Memorable Moment: Public comment section is tense and repeatedly interrupted, displaying deep mistrust.
    • "[CSOCs] are the central nerve centers... to criminalize members of the community." – Stop LAPD Spying Member [03:26]
    • Vote passes despite protests.

2. Predictive Policing Today versus Sci-Fi Precedents

• Minority Report vs. PredPol:
  • Fictional “pre-crime” inspired by Minority Report has partially materialized, but with twists:
    • Real tools forecast property crime, drug offenses, and gun violence—affecting marginalized neighborhoods.
    • Predictive policing isn't about free will or psychics: it's about statistical inference from vast datasets.
    • “In the real world, free will isn’t the issue. The real philosophical problems are more basic. But maybe even harder.” – Barry Lam [08:49]

3. How PredPol Works (Place-Based Prediction)

• Guest Expert: Dr. Sarah Brayne (UT Austin sociologist)
  • PredPol: Analyzes historical property crime (location, time, type); outputs “predictive boxes” officers patrol during uncommitted time.
    • “It’s not that different than what they were doing before…” – Sarah Brayne [12:43]
  • Critique: Risk of “over-policing” the same neighborhoods due to feedback loops, as data comes from police sources.
  • Defenses: Proponents point to race-agnostic, published algorithms akin to earthquake forecasting.

4. The Next Generation: Social Data & Real-time Prediction

• Research Highlight: Flora Salim (RMIT University)
  • Uses crowdsourced Foursquare “check-in” data to predict crime within three hours—with up to 16% better accuracy than PredPol. [17:51]
  • The more diverse, less regular the crowd, the greater the crime prediction.
    • “We managed to have improvement of up to 16%.” – Flora Salim [17:58]
  • Raises concerns about privacy, especially as technologies (e.g., Fitbit) could further monitor both suspects and police stress.

5. Operation LASER: Person-Based Predictive Policing

• Operation LASER:
  • Uses “chronic offender” bulletins generated via algorithms—targeting individuals predicted most likely to offend.
  • Data largely stems from “FI cards” (field interview notes) detailing any police-civilian interaction, including bystanders.
    • “Even people that have never talked to the police and have no direct police contact are included in law enforcement databases.” – Sarah Brayne [27:30]
  • System can generate self-perpetuating surveillance: Police target those with high scores, document further encounters, which build scores further.
    • “…that might turn into somewhat of a self-fulfilling prophecy…where if you’re going out and specifically seeking out the people with high points value…that can very quickly lead to a feedback loop.” – Sarah Brayne [29:12]

6. Community Impacts and Civil Liberties Concerns

• Stop LAPD Spying Coalition’s Critique:
  • Strong evidence that racialized communities—especially Black and brown youth—are over-represented and harmed.
  • Predictive algorithms and associated police discretion create “race-neutral” tools with disparate impact.
    • “We found in 2017, the Black community was five times more often arrested than the white community…data can stand in as a proxy for race.” – Jamie Garcia [33:07]
  • Civil rights issues: Extraction from neighborhoods, stigmatization via nuisance abatement and housing eviction, chilling effects on social mobility and trust in institutions.

7. Philosophical and Legal Dilemmas

• Statistical Evidence vs. Individual Justice:
  • Guest: Renee Bollinger (Philosopher, Australian National University)
    • Warns that “error costs”—the danger of wrongfully targeting innocents—are high even with strong statistics.
      • “…even for probabilities that clear a threshold for reasonable belief…if the error costs are high enough, you shouldn’t treat it as true.” – Renee Bollinger [38:36]
    • The more frequently a generalization is applied, the greater risk of systemic injustice and eroded trust—especially for marginalized groups.
      • “It’s possible for a statistical generalization to be both accurate and unjust. Sometimes the more accurate it is, the more unjust it can be.” – Barry Lam [45:15]
  • Raises the specter of changing standards of “individualized suspicion” in law—will big data undercut probable cause and individualized rights?

8. Larger Societal Implications

• Risk Exposure & Surveillance:
  • Routine encounters with police—even absent arrests—cause affected individuals (disproportionately minorities) to avoid banks, schools, and hospitals, reducing upward mobility and trust in institutions.
    • “…we really need to think about not just what the benefits for increased police efficacy can be, but what the chilling effects of surveillance can be, too.” – Sarah Brayne [46:44]

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Notable Quotes & Memorable Moments

• “Algorithmic objectivity is a fiction, a cover.” – Narrator, summarizing coalition views [05:13]
• “You’re rubber stamping CSOCs and you’re rubber stamping the same goddamn policies.” – Stop LAPD Spying Coalition Member [06:23]
• “Palantir can then give you a social network map for an individual, who in the past they’ve been seen with, cars they’ve driven, where in the neighborhood they’ve been stopped at…” – Narrator [27:15]
• “The most consequential question is whether it’s going to lead to a revolutionary change in the standards of reasonable suspicion and probable cause…” – Barry Lam [36:10]
• “It’s possible for a statistical generalization to be both accurate and unjust. Sometimes the more accurate it is, the more unjust it can be.” – Barry Lam [45:15]

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Timestamps for Major Segments

| Time | Segment | |------------|------------------------------------------------------------| | 01:09 | Introduction to LAPD Police Commission/CSOC | | 03:06 | Tense public comment period | | 07:38 | Minority Report & predictive policing parallels | | 11:04 | Interview: Dr. Sarah Brayne - Explaining PredPol | | 14:51 | Interview: Dr. Flora Salim - Social data & real-time AI | | 22:46 | Hi-Phi Nation bonus content, transition to Operation LASER | | 24:23 | Stop LAPD Spying Coalition confronts LAPD on Operation LASER| | 25:58 | The mechanics of FI cards and chronic offender scoring | | 29:12 | The feedback loop in predictive targeting | | 33:07 | Disproportionate impact (race as a proxy) | | 38:06 | Philosophical critique: statistics vs. individualized suspicion | | 43:39 | Paradox of statistical discrimination & risk exposure | | 45:41 | Chilling effects on communities |

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Tone & Final Thoughts

The episode is at once analytical and urgent—grounded in philosophy but alive with street-level activism and high-stakes debate. It invites listeners to grapple with complex realities: sophisticated predictive tools may promise safety, but at significant costs to civil rights, trust, and equity—especially for the most marginalized.

The discourse is candid, with activists expressing deep anger and academics offering nuanced, sometimes paradoxical insights into how data-driven policing may change the very standards of legal justification and justice.

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For further exploration: The next episode promises to trace predictive algorithms from the streets into the courtroom and prisons, questioning how big data will reshape American notions of guilt, innocence, and due process.

Transcript

[Dalia]

Hi there, it's me, Dalia. And while Amicus is off this week, I'm here to introduce you to the new Slate podcast, Hyphenation. Hyphenation is philosophy in story form. Every episode features stories of extraordinary human experiences and transforms them into an examination of philosophical ideas that challenge our basic assumptions. With the help of philosophers, Hyphenation weaves together narrative storytelling, investigative journalism, and sound design to examine thought process provoking questions you're about to hear. Episode one of the new season called the Pre Crime Unit. In this episode, hi Fi Nation host Barry Lamb, an associate professor of philosophy at Vassar College, explores how the use of algorithms in the criminal justice system is leading to predictive policing and causing some community blowback. Hyphenation has received critical acclaim from the Guardian, Huffington Post, Indiewire, and lots of other places, which is why we're so happy to welcome it into the Slate Podcast network. If you like this episode, subscribe to the show in its own feed. For new episodes, just search for Hyphenation in your favorite podcast app. Thank you so much. Enjoy.

[Barry Lamb]

High from Slately.

[Narrator / Interviewer]

Every Tuesday in downtown LA, the LAPD Police Commissioners have a board meeting that starts at 9:30 in the morning.

[Police Commission Chair / Meeting Moderator]

Please call the roll.

[Sarah Brain]

Good morning.

[Narrator / Interviewer]

Let the record reflect Commissioners Sobroff, Decker, Bonner and Goldsmith are present and we have a quorum. There are five police commissioners, all civilians and volunteers, who were appointed by the mayor. The meetings are open to the public and there's time allocated for public comments on every agenda item. Anyone who requests it gets two minutes to speak to the board. There's a stop clock right in front of the podium that starts a countdown as soon as you start and a bell goes off when your time is up. Then your mic gets cut off. The last meeting of the year 2018 was in mid December, right before the holidays. The first item on the agenda couldn't sound more boring and bureaucratic. The commissioners are being asked to approve a donation from a charitable organization of $35,000 to the LAPD to be used.

[Police Commission Chair / Meeting Moderator]

To reconfigure the existing conference room into a Community Safety and Operations center for the benefit of Operations West Bureau.

[Narrator / Interviewer]

Steve Soboroff is the President of the Police Commission and he's presiding over the meeting. There are four LAPD Central west, south and Valley. The Community Safety Operations center, or CSOC for short, started in South Bureau as a response to a rise in violent crime in South Los Angeles in 2016. Think computers doing data analytics, centralized intelligence sharing, that kind of thing. After a couple of years South Bureau saw a significant reduction in homicides and gun violence. So the mayor, Eric Garcetti, pledged to spread CSOCs to every other bureau in 2018. Time for public comments.

[Police Commission Chair / Meeting Moderator]

Go ahead.

[Sarah Brain]

Good morning.

[Stop LAPD Spying Coalition Member]

Do you know what a CSOC is that you're going to approve $35,000 funding?

[Narrator / Interviewer]

What these operations centers are used for are to gather and store and analyze.

[Stop LAPD Spying Coalition Member]

And share license plate readers, body cameras.

[Narrator / Interviewer]

CCTVs, surveillance data, which is then used.

[Police Commission Chair / Meeting Moderator]

To criminalize members of the community.

[Stop LAPD Spying Coalition Member]

And from there, individuals are identified and targeted.

[Sarah Brain]

Csoc, they prime officers, so that they.

[Jamie Garcia]

Are more likely to use force.

[Barry Lamb]

You are sending officers out into the.

[Narrator / Interviewer]

Field, frightened, and creating this use of force that is resulting in people being killed.

[Stop LAPD Spying Coalition Member]

And that is where CSOCs come in, because they are the central nerve centers.

[Police Commission Chair / Meeting Moderator]

Next speaker, please. Time's over now. Next speaker, please.

[Narrator / Interviewer]

There's probably about 40 people in the room, including myself, flanked by police officers on both sides. And 11 people spoke against the approval of CSOC funding. Many were members of the Stop LAPD Spring Spying Coalition, a group of community organizers who have been suing the LAPD to release documents about their computerized surveillance policies. Afterward, one of the police commissioners, Shane Goldsmith, addressed the concerns before moving for a vote.

[Renee Bollinger]

I will vote on this. Yeah, approve this. But I did want to just acknowledge the concerns that have been raised.

[Police Commission Chair / Meeting Moderator]

It's her turn to speak.

[Renee Bollinger]

My turn. Thank you. I know that nothing short of abolition of the police Department and this commission will satisfy you.

[Police Commission Chair / Meeting Moderator]

We have a motion for approval.

[Narrator / Interviewer]

Please.

[Renee Bollinger]

So moved.

[Police Commission Chair / Meeting Moderator]

Okay. We have a second.

[Jamie Garcia]

Shame on you.

[Police Commission Chair / Meeting Moderator]

All in favor?

[Narrator / Interviewer]

Aye.

[Police Commission Chair / Meeting Moderator]

Anyone opposed?

[Narrator / Interviewer]

Also move.

[Police Commission Chair / Meeting Moderator]

4.

[LAPD Captain]

0.

[Police Commission Chair / Meeting Moderator]

What's next? What's the next one? So I'm going to ask you all to stop disrupting the meeting. If you can't stop disrupting the meeting, then the meeting will stop anyway.

[Narrator / Interviewer]

The approval of funding for the West Bureau CSOC is a small but symbolic step in LA's ongoing move toward predictive policing technologies. The goal is to have computer programs predict who, where, and when the next crime is going to occur, and to direct police units to intervene and prevent it. At this meeting, the CSOC represents, for one side, how a once notorious police department can turn to technology for progress and reform, replacing the prejudice of human judgment with impartial data and algorithms. For the STOP LAPD Spying Coalition. Algorithmic objectivity is a fiction, a cover. For the coalition, the CSOC is just another efficiency tool to target, incarcerate and and control racial minorities in a rapidly gentrifying city. It's a debate that will eventually spread across the country because the technology is moving at a rapid pace and police departments everywhere are looking for an upgrade. One way to anticipate how cities around the country will react is to look at how the debate is unfolding in la.

[Stop LAPD Spying Coalition Member]

You're rubber stamping sea socks and you're rubber stamping the same goddamn policies. You're speaking from both sides of your fucking mouth.

[Police Commission Chair / Meeting Moderator]

So what you're doing is your time is over. You're disrupting the meeting.

[Narrator / Interviewer]

At this point, about six police officers in the room start moving in and they open a digital video recorder that one of them's been holding the whole time and they start filming.

[Stop LAPD Spying Coalition Member]

Of course we want abolition of policing. Of course we want abolition of your sake. Shame on you, Shane Goldschmidt. And you're a fucking president of a free Ladies.

[Police Commission Chair / Meeting Moderator]

That's your last warning or you're both going to be leaving. You're both going to be leaving. Both of them are out.

[Narrator / Interviewer]

Thank you.

[Police Commission Chair / Meeting Moderator]

Thank you. Next speaker, please. Other people would like to speak. Other people follow the rules and would like to speak, but you're taking your time.

[Narrator / Interviewer]

You're out of the meeting.

[Police Commission Chair / Meeting Moderator]

You're continuing to disrupt. Happy holidays to you too.

[Jamie Garcia]

Billionaire piece of speaker.

[Sarah Brain]

The next speaker is Adam Smith.

[Barry Lamb]

From Slate. This is hi Fi Nation philosophy in story form. Recording. From Vassar College, here's Barry Lamb.

[Narrator / Interviewer]

Steven Spielberg's adaptation of the Philip K. Dick story Minority report is now 17 years old. Given what we know today, parts of it were prophetic, parts of it absurd. The film tells the story of a future where Tom Cruise pieces together psychic predictions of violent crimes on a high tech computer, then sends out a team of cops to arrest and jail the perpetrator before the crime occurs. They're called the Pre Crime Unit. Real life predictive policing is here now. The CSOC that the LAPD commissioners funded is the real life equivalent of Tom Cruise's control room. Other than that, actual predictive policing programs show just how unrealistic the movie was. The crimes in the Minority Report were all bourgeois fantasies. Murders of cheating spouses, conspirators, and child kidnappers. All of them had affluent white victims and white perpetrators. In real life, predictive policing technologies target property crime, drug dealing, gun violence associated with gangs, the kind of things affecting communities of poverty and color. The other piece of fantasy in Minority Report is what it depicts as the central problem with predictive policing. People have free will. The psychics might have been wrong. In the real world, free will isn't the issue. The real philosophical problems are more basic. But maybe even harder. And there aren't any psychics, just statistical science based on a little criminology, a little anthropology, a lot of data collection, all pulled together by the developing fields of machine learning and artificial intelligence. In the next two episodes, I'm guiding you through the use of statistical algorithms in criminal justice. From the streets to the prisons, lots of forces are pushing to replace human judgment with computerized ones. And it's happening at a time when the rules aren't known and what counts as justice isn't obvious.

[Barry Lamb]

Hifi Nation will return after these messages.

[Narrator / Interviewer]

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[Sarah Brain]

My name is Sarah Brain and I'm an assistant professor of sociology at the University of Texas at Austin.

[Narrator / Interviewer]

Sarah Brain is going to be my guide. Sarah embedded herself for years with the LAPD doing ride alongs, observations, interviews, studying how these new technologies are changing the relationship between the police and the community. Sarah, let's start with the basics. What's PredPol?

[Sarah Brain]

Sure, PredPol is a location based predictive policing software. So that's used to predict where and when property crime is likely to occur in the future. So essentially it takes three kinds of inputs that are all part of historical crime data. When, where and what type of crime occurred. More recent crimes are weighted more heavily and then it outputs these 500 by 500 square foot boxes where crime is more likely to occur in the future. And then police officers are given these printouts or these images at the beginning of their shift and told to spend time in those predictive boxes.

[Narrator / Interviewer]

Tell me the range of things that officers do in the field with that kind of information.

[Sarah Brain]

I mean, essentially they drive to those boxes and then they check in and out of those boxes during uncommitted time. Meaning like if the officers were not responding to a call, for example, or at the station booking somebody, they were told to spend their uncommitted time driving to those predictive boxes and basically looking around and seeing if anything was happening there. And of course, intercepting if they saw a crime in progress. But a lot of it is just this like deterrent strategy of if you're sitting there in this high crime area, somebody who maybe was gonna steal a car, if there's a cop car sitting right there, wouldn't steal that car.

[Narrator / Interviewer]

It doesn't sound dramatic at all. It sounds really boring. And just what you would expect people to do. Like before you would just guess, like, I guess there's a lot. That's a chop shop over there or something.

[Sarah Brain]

Yeah, exactly. That's the thing is I think that like, the more you actually learn about predictive policing, the more you're like, oh, okay, that's actually, it's not that different than what they were doing before.

[Narrator / Interviewer]

And it's true. Even the Stop LAPD spying coalition admits that these predictive policing technologies are continuations of existing practices that have long been a part of patrolling. The important question is whether that's a good or bad thing. Advocates of location based Predictive algorithms like PredPol claim to have a big advantage over ordinary person based criminal profiling. PredPol never uses data about social categories like race, gender, age or criminal history. In fact, location based systems don't use any identifying information about individuals. And predictive software is a lot more transparent than, say, the human mind. Predpol's equations, algorithms, controlled studies, are all published in peer reviewed academic journals. PredPol uses the same predictive models that geologists use to forecast aftershocks after major earthquakes. It's not clear why that works though. But that's actually one of the points of using predictive algorithms. If a forecast is accurate, it doesn't matter why. But a criticism of PredPol is that it makes law enforcement stuck forecasting crime in the same known neighborhoods and locations, making particular places feel occupied or over policed. And the crime history data that PredPol uses also comes from law enforcement itself. And law enforcement has conflicting incentives that can affect the accuracy of their data. But things are starting to get fancier. PredPol is version 1.0 of location based predictive policing using crime to predict crime. There's no reason why algorithms have to Be so limited.

[Flora Salim]

Hello?

[Narrator / Interviewer]

Hello?

[Sarah Brain]

Can you hear me all right?

[Narrator / Interviewer]

Yeah, I hear you. Can you hear me?

[Flora Salim]

I'm Flora Salim. I'm a senior lecturer at RMIT University School of Science.

[Narrator / Interviewer]

Machine learning Researchers like Flora Saleem can presumably use any data they have to see if a machine can find positive correlations with crime.

[Flora Salim]

We came up with certain handcrafted features that we actually extracted from the check ins data.

[Narrator / Interviewer]

Salim and her colleagues got their hands on check in data from the mobile app Foursquare in Brisbane and New York City. If you don't know about Foursquare, it's an app you use to learn about restaurants, attractions, events and so forth. For a while, it kept check in data on all of its users. The data is anonymized, there's no identifying information. But you do have information about an individual's check in history and you can see how many check ins are at a location and how that changes over time.

[Flora Salim]

So we look at the number of venues and the number of check ins. We look at the diversity of these check ins across locations.

[Narrator / Interviewer]

There are a lot of interesting things you can measure using just check in data. One example is diversity. For instance, you know whether a group of people tend to be into the same things based on their similar check in histories. So if you can see that a lot of these people congregate at location X, then X is a homogenous location. If you find a location where people with very different check in histories are congregating, then it's a diverse location. Another thing you can look at is the ratio of newbies to regulars at a place. And these things change over time as people are moving around the city. So what you do is give the computer all of this check in data. Then you feed it crime reports for crimes like assault, unlawful entry and drug dealing. And you do it with data for a set period, like six months, and let the machine scan all the data, cut it up, look at ratios and changes, and determine how check in patterns are correlated with particular crimes. This is the training phase. Once that's done, the computer has come up with its model for predicting future crime and you move on to the testing phase. You give the computer new check in data that it hasn't seen before. You ask the computer to make a prediction about where and what kind of crime is going to occur. Now here's the really ambitious part of the experiment. Salim and her colleagues were looking to predict new crimes that were supposed to happen within three hours of check in. Their goal is to give officers real time data about where to increase their patrols. PredPol predicts crimes that are supposed to happen within the next day. To see how well your algorithm does, you compare the computer's predictions with the actual crime reports that happened within the next three hours.

[Flora Salim]

You know about predpol, right?

[Narrator / Interviewer]

Yeah, absolutely.

[Flora Salim]

We've improved it much more. We managed to have improvement of up to 16%.

[Narrator / Interviewer]

Here are some of the patterns that the machine found. In general, as locations become less popular, crime increases. Crime is likelier to happen as locations start getting newer or more infrequent visitors. And as locations become more diverse, the likelier you're going to have crime in the next three hours. What humans tend to do is come up with a theory, an explanation of why things pattern the way they do. But this isn't always a good thing. I once gave two lectures about this stuff where in the first one I told the audience the real data, crime decreases as a place becomes more popular. The audience immediately came up with an explanation. More people make it harder to get away with the crime, so criminals target quieter areas. But in the second lecture, I said the opposite, that crime increases as more people check in. And the audience immediately had an explanation for that. More people mean more opportunities for crime. This is a virtue and vice of human judgment. We're good at explanations and we're good at being convinced of our explanations, letting them guide our thinking whether they're true or not. The machines don't do this. They only make predictions, leaving the stories for the humans. Now, AI researchers aren't so naive to think that their algorithms are unbiased or story free. The claim though, is that they're far less biased than people who get too caught up in the stories they tell themselves and often ignore data completely. Salim and her colleagues were able to get accuracy without needing crime histories or locations and times. The mere movement patterns were enough. Who knows how accurate things can get when you start putting all of this data together or bring in new kinds of data?

[Flora Salim]

To be very hypothetical, we wear these fitness trackers. A lot of them actually have sensors that track heart rates. It also tracks your mood, your emotions. If you can correlate a lot of things, for example, your galvanic skin response, which is basically how much you sweat and all that. That also can tell some signals to do with your stress level. Even something more fine grained on that level can potentially even boost accuracy even more.

[Narrator / Interviewer]

A lot of people I talk to get creeped out at this point. But Fitbit data has already been used to solve crimes, Pinpointing exact times of death One sociologist even proposed that we use Fitbits to monitor the police, to predict those that are likeliest to have unusually high stress responses and to easily use force. It's a hard question for all of us how much data about ourselves we're willing to give up in the interest of public safety. But the unfortunate reality is that some communities have more power than others in settling this question. Government officials, police forces, affluent suburbanites, they generally win fights over how much they get to be surveilled. The actual issue is probably how much the affluent will sacrifice the privacy of the poor to secure their own safety. And on the ground, these aren't just theoretical concerns. Hey, hey. Ho ho. Seesaw has got to go. Just hours after the police commissioners meeting, the Stop LAPD Spying Coalition got together at their headquarters in Skid Road to organize a protest and occupation of the seesaw located in the central bureau.

[Stop LAPD Spying Coalition Member]

We leave for the procession at 3:30.

[Narrator / Interviewer]

Their goal was to have a protest group march to the central police station and have a smaller group beforehand enter the station and demand to know if a CSOC was present and if so, to have its commander publicly confirm or deny the many practices they have on record as part of LAPD Predictive Policing.

[Jamie Garcia]

By the time you guys start marching, we're inside already shaking them up.

[Narrator / Interviewer]

They were planning to do this in just two days and I followed them along and followed them in.

[Jamie Garcia]

We're gonna go inside.

[Stop LAPD Spying Coalition Member]

It's about the dismantling.

[Jamie Garcia]

So you are denying that a sea sock exists.

[LAPD Captain]

We will use those statistics to determine where we need to send resources.

[Barry Lamb]

We'll return to the rest of hi Fi Nation after these messages.

[Narrator / Interviewer]

If you're a new or longtime listener of hi Fi Nation, you'll know that we just joined Slate. I wanted to let you know that this season there's going to be bonus content exclusively for Slate plus members. Slate plus members get ad free episodes of all Slate podcasts, including this one. And this season I'll be producing bonus segments, outtakes from the show, extended interviews and original panel discussions with me and a guest on the philosophical issues from an episode. You can sign up for Slate plus by clicking the link in the show notes slate.com hifi plus get two weeks free and $35 for your first year. That's slate.com hifiplus. The other predictive policing program the coalition is protesting IS Operation Laser, LAPD's Person Based Predictive policing program. This particular program is far more secretive.

[Jamie Garcia]

We want them to come out, answer questions the community has.

[Narrator / Interviewer]

Jamie Garcia is one of the lead organizers of the STOP LAPD Spying Coalition. She's leading the team that is entering the central station to expose the CSOC and to ask its commander whether there's a secret list of names rumored to be part of Operation Laser. This list, the Chronic Offender Bulletin, is supposed to be a daily list of people who are predicted by an algorithm to be that day's likely offenders.

[Jamie Garcia]

I'm with the STOP LAPD Spying Coalition. I was told that there's a community safety Operations center that's here. Is that true?

[Narrator / Interviewer]

I'm aware of us.

[Jamie Garcia]

So are you denying that a community Safety Operations center is here?

[Narrator / Interviewer]

I'm not denying.

[Sarah Brain]

I'm just not aware of it.

[Narrator / Interviewer]

CSOC is a racial program used to collect data on black and brown bodies. We want it shut down for the simple fact that it is contributing to the execution of our folks in our communities. The captain in charge of the CSOC program has decided to come down and is willing to come out to the community to talk to them about it.

[LAPD Captain]

We had run an operation which is merely a group of people to get together, look at where the crime is being committed, and we make determinations where to best use police resources. That is what you're referring to as the csoc. That current practice is not currently in use at the moment.

[Jamie Garcia]

So are you telling us that there is no secret list of chronic offenders.

[LAPD Captain]

No list of chronic offenders in practice right now?

[Renee Bollinger]

Shut it down.

[Narrator / Interviewer]

Shut it down. Tell me about Operation Laser.

[Jamie Garcia]

Operation Laser is a person and place based predictive policing program which stands for.

[Sarah Brain]

Los Angeles Strategic Extraction and Restoration.

[Narrator / Interviewer]

Sarah Brain Sociologist at UT Austin Sarah observed the practices of Operation Laser from the ground. And she considers the most important element of this predictive program not to be some fancy AI learning program, but a simple index card called an FI card.

[Sarah Brain]

There are these really important data collection tools, and funny enough, they're used for all of these other things too. Like they're just these small index cards. So they're used to like stuff a lock in a gate if you don't want it to lock behind you when you're going into a house, for example. Anyway, these FI cards are everything. And that's basically where you write down any information that comes up in the course of an interview with a civilian.

[Jamie Garcia]

Those cards are being used to map communities, to find out what kind of informal social networks exist in communities in order to disrupt them.

[Narrator / Interviewer]

Jamie Garcia of the STOP LAPD Spying.

[Jamie Garcia]

Coalition, in order to find out where and through their own language is where goods and information gets most exchanged through finding those Nexuses and disrupting them. Regardless if you are deemed a perpetrator or a victim, even being in the surrounding area, being a witness opens you up to being mapped as well.

[Narrator / Interviewer]

This isn't just suspicion or paranoia. The field interview cards contain information about who is in the car with someone during a stop who might be across the street, the neighbor who walked by. People who aren't even questioned by the LAPD but are observed to be in the vicinity can be put onto an FI card and then entered into the system. Car information, make, model, condition, they're all recorded too. And what's new is that all of this information daily is entered into the system, which officers can then run through software called Palantir. Palantir can then give you a social network map for an individual who in the past they've been seen with cars they've driven, where in the neighborhood they've been stopped at, and so forth.

[Sarah Brain]

Even people that have never talked to the police and have no direct police contact are included in law enforcement databases. Now, you can't drive your car on a street in LA without being picked up eventually by an automatic license plate reader.

[Narrator / Interviewer]

But it's what LAPD is doing with all of this social network and mapping information that is most concerning to the STOP LAPD spying coalition. They're put into a computer algorithm to.

[Jamie Garcia]

Generate a daily list, the chronic offenders. And the chronic offender uses what's called a risk assessment. So you get five points if you've been arrested with a handgun. You get five points if you have a violent crime on your rap sheet. You get five points if you've been on parole or probation.

[Sarah Brain]

And then five points for gang affiliation. And then people get one point for every police contact. So every time the police stop somebody and fill out an FI or a field interview card on them, individuals get another point added to their score. And then just like with the place based predictive policing where officers at the beginning of their shift are given these printouts, similarly, officers are given these lists of what are called chronic offenders. People that have the highest points values in a division that day. And you'll just sort of drive to a park, say that's like a known area for drug deals. And you'll say, oh, hey, like some of these guys are on our list, let's see if we can go talk to them, do a consensual stop, try and collect some intelligence on them through that.

[Narrator / Interviewer]

Will they fill out a card?

[Sarah Brain]

Yes, they would fill out a card for that.

[Narrator / Interviewer]

So they would look for somebody who's on a list and then fill out a card after another interaction with them.

[Sarah Brain]

Yes.

[Narrator / Interviewer]

Okay. Does that mean they get another point?

[Sarah Brain]

Yes.

[Narrator / Interviewer]

Should I be worried about that? I mean, I guess ringing a bell.

[Sarah Brain]

You can see how that might turn into somewhat of a self fulfilling prophecy, for sure. Or like a feedback loop, if you will, where if you're to going, going out and specifically seeking out the people with high points values and then you go and stop those people and then that increases their points value, that can very quickly lead to a feedback loop.

[Narrator / Interviewer]

It sounds like it's more than can. I mean, it seems like it does. Right.

[Sarah Brain]

So I don't actually have the data that on people's points, but like, I would definitely think that would be the case.

[Jamie Garcia]

Yeah, we did get through a public records request, one chronic offender bulletin release where we can actually see what it looks like. On that request, you see a person actually being stopped about three or four times in one day. At that point, that person already has four points on them. We were able to access some part of a list from MacArthur park of people who were identified as chronic offenders. That people as little as six points that they had tallied and were as young as 19.

[Narrator / Interviewer]

Official laser policy is that you get a point for a field interview only if it's a quality police interaction. But there's no definition of what quality means. The E in laser stands for extraction. And that's the ultimate goal, getting the people on the chronic offender lists out of the neighborhood before they commit another crime. And there's another feature of LAPD data collection. All of this crime data allows you to identify addresses, businesses, street corners that seem to have high frequency interactions with police. They're called crime anchor points. Just like there's a goal of extracting a criminal with a high risk score from the area, there's also a goal of ridding an area of an anchor point where LAPD works with the city attorney to evict or relocate people through the citywide nuisance abatement program.

[Jamie Garcia]

I think that what's insidious about this, when you get an eviction notice, you cannot, it's impossible to rent again. You become stigmatized. And so, especially in a city where affordable housing is almost not possible to find, especially in a city where the homeless population is somewhere around 55,000 people, they are now using this kind of pseudoscience to demonstrate that a property or people in a property should be removed. That's the component of it where it's like they're killing us softly.

[Renee Bollinger]

Now.

[Narrator / Interviewer]

The designer of Operation Laser turned down my Request for an interview. But I think I can charitably reconstruct his reasoning. A very small number of people are disproportionately responsible for crime in a neighborhood. When you strategically extract high probability offenders in a laser focused way, you're making neighborhoods safer with less collateral damage. And you're doing it in a race neutral way by only calculating probability of future offense using data that is correlated with future crime. If the consequence happens to be that young black and brown males are the only ones affected, it's because they're the ones satisfying all the race neutral conditions of being likely offenders.

[Jamie Garcia]

When we look at all the different elements that are used to calculate the risk assessment of a potential chronic offender, we found in 2017, the black community was five times more often arrested than the white community. When you look at stops, which essentially are what lead to field interview cards being filled out, the black community was five times more likely to be stopped, even in parole and probation. And we're finding more and more studies that are identifying that the black community is more often to be on parole or probation. So even though you claim race neutrality data can stand in as a proxy.

[Narrator / Interviewer]

For race, the other perspective has some merit. Too many of the things that get you points in the algorithm are things under police control. If police want to give someone points, they just have to start talking to them and then call it a quality interaction. Gang affiliation is another example. Who gets recorded as being affiliated with a gang member? An officer could connect just about everyone to a gang member if a neighborhood has gangs in it. When I was young, I lived next to gang members, rode the bus with them. I mean, every teacher in LAUSD probably teaches a gang member. So who gets the points for being affiliated with a gang member after an FI interview? The skinny Asian neighbor or the Latino neighbor? And finally, there's the issue of civil rights. How can LAPD legally extract someone for just having been predicted by an algorithm to be a future offender? Did you observe anything that looked like predictive arrest? A kind of intervention that seems like you would need some kind of probable cause or reasonable suspicion in order.

[Sarah Brain]

I didn't see them, you know, violating requirements for reasonable suspicion or probable cause, for example? It was really more of these consensual stops. It's not illegal for a cop to go up to anybody and start talking to them. You can say, I don't want to talk to you and walk away. But if you are a known gang affiliate and you're on parole or probation, you don't have the same ability to walk away that then in the course of a consensual stop, you might then see something that would constitute individualized suspicion to give you reasonable suspicion to actually then question someone.

[Narrator / Interviewer]

I mean, I guess another way of putting it coming from the other side is why isn't a high score enough for individualized suspicion or probable cause? Right, yeah, way of putting it.

[Sarah Brain]

Yeah. I mean, and I think this is like kind of just as much as it's a legal question. It's kind of this like philosophical question around, like, what is individualized suspicion? Predictive policing is really probabilities that this person has a higher probability of committing a crime in the future based on past data than this other person. Whereas individualized suspicion is not supposed to be probabilistic, it's supposed to be observable then and there. So just because somebody has done something in the past doesn't give you individualized suspicion now in the present. But actually, like, I think that those ideal typical categories are kind of like getting conflated now in this like, world of predictive policing. Like what even is individualized suspicion? Is your same action interpreted as more suspicious if you're inside a predictive box or if you have a high risk score than if you don't? I think this binary is not really there anymore. It's, it's eroding. At least.

[Narrator / Interviewer]

There's a thin line separating the legitimate and illegitimate use of police power. We give it names like individualized suspicion, reasonable suspicion, and probable cause. The biggest change from predictive policing, as I see it, isn't whether it's going to lead police to profile, identify and target particular people and locations. That's just what they do. The most consequential question is whether it's going to lead to a revolutionary change and in the standards of reasonable suspicion and probable cause and whether that is going to be morally legitimate.

[Sarah Brain]

You're not allowed to stop somebody just because they're black. Right? But you are allowed to talk to somebody because they have a high point score. Now that it's quantified, it's like more difficult to contest legally or to really put your finger on. Where exactly is the bias there?

[Narrator / Interviewer]

Suppose you use gun and ammunitions purchasing data and past history of domestic violence to determine likely mass shooters, and the algorithm spits out a few names. Is that sufficient suspicion for a stop search and surveillance? Or better yet, the same algorithm used to determine who is likeliest to commit a crime is used to offer targeted job, educational and social services. Can you make a claim of statistical bias here when it's the same statistical algorithm doing the work. Prior to the era of big data, the courts have been very skeptical of allowing statistical evidence to satisfy legal standards of justification, something I'm going to cover on the next episode. But the Supreme Court has already signaled that they need to revisit all of these questions if in the era of big data.

[Renee Bollinger]

I'm Renee Bollinger. I'm a postdoctoral researcher at the Australian National University.

[Narrator / Interviewer]

Renee Bollinger is one philosopher among many who is thinking about the connection between statistical evidence and what people do with that evidence, particularly in matters of race and gender profiling. There's a seemingly unquestionable assumption that what statistics gives us is what is likeliest to be true. And the likelier something is statistically, the more justification we have for treating it as being true. Bollinger argues that this isn't always the case.

[Renee Bollinger]

What people don't tend to realize is that even for probabilities that clear a threshold for reasonable belief so it's 90% likely or 80% likely, or something quite high, if the error costs are high enough, you shouldn't treat it as true. To do so would be to risk mistreating a particular person.

[Narrator / Interviewer]

For example, if you teach a class where everyone gets a perfect on their test and you get evidence that 90% of the class cheated, you have to decide who to punish. And statistically, if you punish everyone, you'll be 90% accurate. But the error cost is that you've treated the 10% who didn't cheat unjustly. Just by relying on this statistical evidence.

[Renee Bollinger]

You'Ll be treating them in a way inappropriate to them. So that's the risk of a false positive. And then you weigh that against the risk of a false negative. So what if it is true and you don't treat them as though it's true?

[Narrator / Interviewer]

Imagine that you decide to treat one of your students as though they didn't cheat, but in fact they did. You let them get away with something.

[Renee Bollinger]

The point that often goes unnoticed is that very often in these statistical inference cases, the costs of the false negatives, the failing to treat it at as true even if it is, are not that high, while the cost of the false positives treating the person as though this is true of them when it isn't, are actually quite high. And so you risk doing something seriously wrong to that person when it's actually false of them. So then what that should do, I argue, is it raises the evidential bar for how sure we have to be before it's okay to accept this proposition as true of that individual.

[Narrator / Interviewer]

Bollinger's view is that even a statistically sound generalization, the kind that you might get from a big data analysis, doesn't automatically allow you to cross some threshold for treating something as being true. Let's take reasonable suspicion, for example. One way to see how low the threshold for reasonable suspicion can be is to imagine 10 people. Nine are known to be innocent, and one is known to be guilty of something like shoplifting. But then the one person runs into a crowd of the other nine, and they all look similar enough that an officer can't distinguish them. Does an officer have enough grounds to search and frisk all 10 to find the one shoplifter? If you think so, then 10% certainty is enough for reasonable suspicion. Now, if a predictive policing algorithm has a 10% threshold and then put some person's name on a list, there's a sense in which we've passed the usual threshold. And so officers should be allowed by law to stop, question and search. Bollinger's view is that this isn't right even if the statistical analysis is correct.

[Renee Bollinger]

One of the big problems with using statistical evidence is that you end up increasing the chances that you'll get the wrong verdict for someone. So if you think, look, it's the nature of criminal justice system that sometimes we'll falsely convict someone, sometimes we'll sentence someone to far longer than they're due, in some sense that's fine, but you should be worried when they start to concentrate on particular groups. And the trouble with statistical evidence is that it tends to reinforce these sorts of concentrations.

[Narrator / Interviewer]

One of the examples philosophers use to highlight this is an episode involving John Hope Franklin, the eminent African American historian. Franklin was at a fancy banquet in his honor in Washington, D.C. where it turned out that all of the service staff was black and the guests were mostly white. Now, statistically, at this banquet, if you're black, you're likelier to be a service staff member rather than a guest. Now, suppose a rich white lady decides that she needs to hand her coat to one of the staff staff, but she doesn't know what John Hope Franklin looks like. Now, she is statistically in the clear if she just makes the assumption that any black male she sees is a staff member. But she shouldn't do that. She is risking the mistreatment of John Hope Franklin and in fact, contributing to an ongoing mistreatment of him. Because it turns out that making the statistical generalization will always mean that John Hope Franklin spends his life being assumed by others to be the help the analogy is that in the criminal justice system, even if the statistical evidence crosses the proper threshold, say 10% of the young black males in a location are responsible for all of the property crime. Using that stat as sufficient to stop and frisk young black males means you are subjecting all of the innocent young black males to ongoing mistreatment. And that's 90% of them. And this will hold even if you flip the numbers. So if 90% are responsible for the crimes, accepting the generalization as true means you're still subjecting all of the innocent young black males to ongoing mistreatment, 10% of them.

[Renee Bollinger]

And so then members of that group face just a higher risk of suffering the false positive. And that's a fairness based consideration against using this kind of evidence.

[Narrator / Interviewer]

It almost sounds like the higher the statistical generalization, the more we shouldn't believe of any individual. I mean, it's paradoxical, but it sounds that way. So if you're part of the 30% minority and 70%, there's something true about them. You're likelier to be treated as though you're the 70 than you are if it was like a 50, 50 case. So it's like the injustice is likelier, which makes belief even harder to get to.

[Renee Bollinger]

Yeah, yeah. So as you get these, these more imbalanced probabilities, one of the other things that that's tracking is that it's a more and more commonly held assumption about this group of individuals. So they are exposed to the risk of this error more and more often and shaping more and more of their life. And so, yeah, as these disparities start to grow, that's a moral reason to not rely on them and to not use them as the basis for belief.

[Narrator / Interviewer]

Wow. I mean, that's a very interesting and strangely paradoxical conclusion. Right. The more likely you are statistically to have some feature, the less likely it should be for other people to believe that you have that feature.

[Renee Bollinger]

Yeah. So long as the feature is one that is morally weighty. So if it's just a question about how likely you are, given that you're British to like cricket, you know, maybe not a lot hangs on that, so it might be fine. But yeah, if it's how likely you are, given that you're black, that you might be a criminal, the higher those probabilities go up, the more moral reason we have not to base beliefs on them.

[Narrator / Interviewer]

Bollinger and many others like her argue that it's possible for a statistical generalization to be both accurate and unjust. Sometimes the more accurate it is, the more unjust it can be. Because the better your statistics, the better the chances that you'll treat the exceptions unjustly. And she's not willing to take that risk. Why is risk exposure itself bad?

[Renee Bollinger]

People who have been exposed to risks have a lower well being as a result of that directly. If you know that you've been exposed to a risk, then that can have a lot of effects on your behavior. You can do things like avoid other sources of risk in your life or do things that are likely to mitigate the harm that you've been exposed to.

[Sarah Brain]

I found that individuals who have been stopped by the police or arrested, and definitely those that have been convicted or incarcerated, are systematically avoiding institutions that collect data on them, surveilling institutions where the police might be able to access that information. And these are really important institutions like hospitals, banks, formal employment and schools. And so if you have have this whole swath of people that are avoiding these legitimate institutions out of concern of law enforcement surveillance, that's impeding their, you know, upward economic mobility, their social integration, all of these kinds of things. And so I think that, like, we really need to think about not just what the benefits for increased police efficacy can be, but like, what the chilling effects of surveillance can be, too.

[Narrator / Interviewer]

Next time on hi Fi Nation.

[Jamie Garcia]

One in five of all people in jail and prison in the United States are people that are detained before trial.

[Narrator / Interviewer]

People plead guilty to get out of jail. And everybody knows this is how the system works. We pretend that it's about justice. It's not. I follow predictive algorithms into the courtrooms and even prisons to see how they're being used to determine who to incarcerate.

[Barry Lamb]

Hyphenation is written, produce, produced and edited by Barry Lam, associate professor of philosophy at Vassar College for Slate Podcasts. Editorial director is Gabriel Roth. Senior Managing producer is June Thomas. Senior producer is TJ Raphael. Production assistants this season provided by Jake Johnson and Noah Mendoza. Gout Visit hyphenation.org for complete show notes, soundtrack and reading list for every episode. That's hip hop. Phination.org.