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Abstract
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Before
We designed a "blind charging" algorithm that automatically redacts race-related information from incident reports to prevent racial bias from influencing prosecutorial charging decisions. After successful pilots with two district attorneys, new legislation requires that prosecutors across California must use race-blind charging by 2025. This pending expansion, alongside high levels of interest from prosecutors across the country, makes blind charging a pressing policy issue that deserves further study—particularly in how its rollout affects Black, Hispanic, and other marginalized individuals and their communities. In a randomized control trial (RCT), we will test whether the use of our algorithm reduces bias in charging decisions or causes any unintended adverse impacts (e.g., increases in charging rates for all individuals). Alongside this RCT, we will run a survey to measure changes in perceptions of procedural justice.
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After
We designed a "blind charging" algorithm that automatically redacts race-related information from incident reports to prevent racial bias from influencing prosecutorial charging decisions. After successful pilots with two district attorneys, new legislation requires that prosecutors across California must use race-blind charging by 2025. This pending expansion, alongside high levels of interest from prosecutors across the country, makes blind charging a pressing policy issue that deserves further study—particularly in how its rollout affects Black, Hispanic, and other marginalized individuals and their communities. In a randomized control trial (RCT), we will test whether the use of our algorithm reduces bias in charging decisions or causes any unintended adverse impacts (e.g., changes in charging rates for all individuals). We will also estimate the amount of additional time it takes attorneys to conduct race-blind review.
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Trial Start Date
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June 01, 2023
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January 01, 2025
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Last Published
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Before
May 03, 2023 04:06 PM
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After
December 01, 2024 10:49 AM
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Intervention Start Date
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Before
January 01, 2024
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After
January 01, 2025
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Primary Outcomes (End Points)
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Before
The primary outcome of interest for our RCT is whether race-blind reviews reduce differences in charging rates between non-white and white arrestees. Our secondary outcome of interest is whether race-blind reviews increase overall charging rates (e.g., if the lack of personal information like names or neighborhoods causes prosecutors to lose empathy).
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After
The primary outcome of interest for our RCT is whether race-blind reviews reduce differences in charging rates between Black or Hispanic arrestees and all other arrestees.
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Randomization Unit
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Before
Randomization by case
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Randomization by case. Different research sites may decide to randomize at different rates (e.g., 10% of cases to control instead of 50% of cases).
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Planned Number of Clusters
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Before
N/A
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After
5 research sites (see note below about site withdrawal).
4 research sites will randomize 50% of cases to each arm; with the remaining site randomizing only 10% of cases to control, and the remainder to treatment.
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Planned Number of Observations
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Before
13,000 cases
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After
Site 1: 10,000 cases, with 5,000 in control
Site 2: 2,000 cases, with 1,000 in control
Site 3: 7,500 cases, with 3,750 in control
Site 4: 24,700 cases, with 2,470 in control
Site 5: 4,800 cases, with 2,400 in control
This is 49,000 cases in total.
We expect the final number may vary considerably depending on site willingness to continue participation in our experiment.
We will run the experiment until we obtain the target number of cases, until two years have elapsed since the start of the experiment in that site, or until a site no longer wishes to participate in our experiment, whichever comes first.
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Sample size (or number of clusters) by treatment arms
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Before
6,500 cases in control
6,500 cases in treatment, under race-blind review
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34,380 cases in treatment, under race-blind review, and 14,620 cases in control.
Note these may change closer to the launch date as sites finalize what proportion of cases they plan to send to treatment and control.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
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After
We ran 1,000 power simulations to determine statistical power for our experiment. In each simulation, we incorporated estimates of each site's charging rate and proportion of Black/Hispanic and all other arrestees based on historical data from each site. We also estimated each site's number of cases they will send to our experiment each year, as well as the proportion of cases they will send to control, based on recent conversations with each site. We assumed that pre-existing charging rates for Black and Hispanic arrestees were 2.75pp higher than the charging rate for white arrestees, and that the treatment arm reduced 90% of this gap.
We generated a synthetic experiment population for each experiment simulation using these parameters. We then fit a logistic regression model to the synthetic population of the form logit(Pr(Y = 1)) = β_0 + β_1 * race + β_2 * treatment + β_3 * race * treatment, where "race" was 1 if the arrestee were Black or Hispanic, and 0 otherwise; and where "treatment" was 1 if the case was randomly assigned to the treatment arm. We then calculated whether the 95% confidence interval for β_3 crossed zero. Under this setup, we expect to detect the primary effect—a reduction in bias in charging decisions—in 81.3% of experiments of this size and design, indicating adequate power to detect small reductions in charging rate differences for Black and Hispanic arrestees.
For our secondary outcome, we ran a similar set of simulations with no pre-existing disparity, instead assuming that charging rates for all arrestees were 2.25pp higher or lower in the treatment arm. We then fit a logistic regression model to the synthetic population of the form logit(Pr(Y = 1)) = β_0 + β_1 * race + β_2 * treatment. This is nearly identical to the above model, though we dropped the interaction to simplify the power analysis. We then examined the coefficient β_2 and calculated whether the 95% confidence interval crossed zero. Given this setup, we expect to detect this effect in 80.5% of experiments of this size and design, again indicating adequate power to detect small changes in the overall charging rate.
We expect to gain additional statistical power by including a random effect for the prosecutor assigned to make the charging decision and by adjusting for case covariates, including arrestee sex, and age; the day, month, and year of the arrest; the presence of flags on the incident report indicating e.g., domestic violence, elderly victims, gang involvement, weapons, or the use of a body-worn camera; the Census-derived racial composition of the area in which the incident occurred, if the address is available; the precinct or police department where the arrest occurred; two-year retrospective arrest and felony arrest counts for the suspect; the alleged charges; and the number of alleged charges in total.
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Secondary Outcomes (End Points)
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Before
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After
Our secondary outcome of interest is whether race-blind reviews change overall charging rates (e.g., if the lack of personal information like names or neighborhoods causes prosecutors to lose empathy, increasing charging rates overall; or if the race-blind stage causes prosecutors to make decisions that lower the risk of a decision reversal in either direction).
We also plan to measure the amount of time it takes to conduct a race-blind review, and compare it against a similar estimate for the status-quo review procedure.
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