The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index; 4 of the 17 sources its claims cite were checked for support.
A large-scale analysis of racial disparities in police stops across the United States
Open accessAbstract
We assessed racial disparities in policing in the United States by compiling and analysing a dataset detailing nearly 100 million traffic stops conducted across the country. We found that black drivers were less likely to be stopped after sunset, when a 'veil of darkness' masks one's race, suggesting bias in stop decisions. Furthermore, by examining the rate at which stopped drivers were searched and the likelihood that searches turned up contraband, we found evidence that the bar for searching black and Hispanic drivers was lower than that for searching white drivers. Finally, we found that legalization of recreational marijuana reduced the number of searches of white, black and Hispanic drivers-but the bar for searching black and Hispanic drivers was still lower than that for white drivers post-legalization. Our results indicate that police stops and search decisions suffer from persistent racial bias and point to the value of policy interventions to mitigate these disparities.
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Claims the paper makes
38 Own result · 17 Own method · 14 Own interpretation · 8 Sources not found · 3 Partly supported · 3 No basis given · 2 No full text of the sources · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“it suffers from the problem of infra-marginality 8,26,27 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Supportedcites [8] Anwar, S. & Fang, H. An alternative test of racial prejudice in motor vehicle searches: theory and evidence. Am. Econ. Rev. 96, 127–151 (2006). sourceExperimental — accuracy not yet measured
The Introduction (p. 4) explicitly identifies infra-marginality as a problem for the outcome test: researchers observe average search success rates, not the marginal rates the test requires. It notes that the problem limits empirical applications of the test. (LLM, conf=0.93)
- Not checked — no full text of the sourcecites [26] Ayres, I. Outcome tests of racial disparities in police practices. Justice Res. Policy 4, 131–142 (2002). source
- Partly supportedcites [27] Knowles, J., Persico, N. & Todd, P. Racial bias in motor vehicle searches: theory and evidence. J. Polit. Econ. 109, 203–229 (2001). sourceExperimental — accuracy not yet measured
Section 2.3 (page unavailable) explains the infra-marginality problem: when guilt probabilities are exogenous, observed average guilt rates do not identify the marginal motorists relevant to a test for prejudice. But the paper distinguishes that case from its own outcome test, which relies on motorists responding to search probabilities. The unqualified claim omits that distinction. (LLM, conf=0.87)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the threshold test indicates that the bar for searching black and Hispanic drivers is generally lower than that for searching white drivers across the municipal police departments and states we consider.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Search policies do not differ within the geographic subdivisions considered.
- Rests on an assumption: Officers do not disproportionately suspect more serious criminal activity when searching black and Hispanic drivers than when searching white drivers.
- Rests on an assumption: The Bayesian threshold model's functional form and prior distributions are adequate for identifying search thresholds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In all cases, we found qualitatively similarly results that are suggestive of racial bias against black drivers in stop decisions”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In aggregate across cities, the inferred threshold for white drivers is 10.0% compared to 5.0 and 4.6% for black and Hispanic drivers, respectively.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“We note that these results are consistent with self-reported stop rates by white, black and Hispanic drivers who participated in the national PPCS 1 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Not matchedcites [1] Davis, E., Whyde, A.& Langton, L. Contacts between Police and the Public, 2015 (Bureau of Justice Statistics, 2018); http://www.bjs.gov/index.cfm?ty=pbdetail&iid=6406
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find α s = -0.033(95% CI [-0.039, -0.027], P < 0.001) and α c = -0.039(95% CI [-0.045, -0.033], P < 0.001)”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In these jurisdictions, stopped black and Hispanic drivers were searched about twice as often as stopped white drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For each time period, the plot shows a marked drop in the proportion of drivers stopped after dusk who are black, suggestive of discrimination in stop decisions.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find α s = -0.033(95% CI [-0.039, -0.027], P < 0.001) and α c = -0.039(95% CI [-0.045, -0.033], P < 0.001)”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among stopped drivers, we found that black and Hispanic individuals were, on average, searched more often than white individuals.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 17
- It may state two separate claims as one. 38
- The quote only partly states this claim. 2
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“the threshold test has been proposed as a more robust means for detecting discrimination 7,22 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Supportedcites [7] Simoiu, C., Corbett-Davies, S. & Goel, S. The problem of infra-marginality in outcome tests for discrimination. Ann. Appl. Stat. 11, 1193–1216 (2017). source
The Introduction describes the threshold test as a new test for discrimination that mitigates theoretical limitations of outcome analysis. The Conclusion likewise says it addresses shortcomings of the outcome test, supporting the comparison in the claim. (LLM, conf=0.93)
- Not matchedcites [22] Pierson, E., Corbett-Davies, S. & Goel, S. In International Conference on Artificial Intelligence and Statistics (eds Storkey, A. & Fernando Perez-Cruz, F.) 96–105 (PMLR, 2018).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“thresholds cannot be identified by the observed data alone 7 , and so inferences are dependent on the specific functional form of the underlying Bayesian model, including the prior distributions.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Partly supportedcites [7] Simoiu, C., Corbett-Davies, S. & Goel, S. The problem of infra-marginality in outcome tests for discrimination. Ann. Appl. Stat. 11, 1193–1216 (2017). sourceExperimental — accuracy not yet measured
Section 2.3, “Inferring search thresholds” (page unavailable), specifies a Bayesian model with priors, supporting the claim that inference uses modeling assumptions. But a passage reproduced under “Conclusion” says the model would be identified by priors rather than data *without* certain constraints; it does not say thresholds cannot be identified from observed data under the specified model. The paper also says its results are robust to the exact choice of priors, qualifying the claimed dependence on them. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The Bayesian threshold model's functional form and prior distributions are adequate for identifying search thresholds.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“in the six municipal police departments we consider, the search rates were 9.5% (95% CI 9.4-9.6%)for black drivers, 7.2% (95% CI 7.0-7.3%)for Hispanic drivers and 3.9% (95% CI 3.8-3.9%)for white drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the gap in hit rates between white and Hispanic drivers was 7.6% (95% CI 6.7-8.6%,P < 0.001), and the gap in hit rates between white and black drivers was 2.6% (95% CI 1.9-3.4%,P < 0.001).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimated gaps in search thresholds between white and non-white drivers are large and statistically significant: the 95% credible interval for the Hispanic-white difference is (-6.4,-4.4%) and the corresponding interval for the black-white difference is (-6.1, -4.0%).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Aggregating across state patrol stops, contraband was found in 32.0% (95% CI 31.6-32.4%) of searches of white drivers compared to 24.3% (95% CI 23.5-25.2%) of searches of Hispanic drivers and 29.4% (95% CI 28.7-30.0%) of searches of black drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“aggregating across municipal police departments, contraband was found in 18.2% (95% CI 17.8-18.7%) of searches of white drivers compared to 11.0% (95% CI 10.6-11.5%) of searches of Hispanic drivers and 13.9% (95% CI 13.7-14.2%) of searches of black drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The gap in municipal search rates between black and white drivers was 5.6% (95% CI 5.5-5.7%,P <0.001) and the gap in search rates between Hispanic and white drivers was 3.3% (95% CI 3.1-3.5%,P <0.001).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly across states, the inferred threshold for white drivers is 20.9% compared to 16.0% for black drivers and 13.9% for Hispanic drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“among state patrol stops, the annual per-capita stop rate for black drivers was 0.10 compared to 0.07 for white drivers; and among municipal police stops, the annual per-capita stop rate for black drivers was 0.20 compared to 0.14 for white drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the gap in search rates between black and white drivers was 2.4% (95% CI 2.3-2.5%,P < 0.001) and the gap in search rates between Hispanic and white drivers was 2.2% (95% CI 2.1-2.2%,P < 0.001).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For Hispanic drivers, however, we found that stop rates were lower than for white drivers: 0.05 for stops conducted by state patrol (compared to 0.07 for white drivers) and 0.09 for those conducted by municipal police departments (compared to 0.14 for white drivers).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the eight state patrol agencies we consider, search rates were 4.3% (95% CI 4.2-4.4%)for black drivers, 4.1% (95% CI 4.0-4.1%)for Hispanic drivers and 1.9% (95% CI 1.9-1.9%)for white drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the gap in hit rates between white and Hispanic drivers was 7.2% (95% CI 6.6-7.8%,P < 0.001) and the gap in hit rates between white and black drivers was 4.3% (95% CI 3.8-4.8%,P < 0.001).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our primary dataset thus consists of approximately 95 million stops from 21 state patrol agencies and 35 municipal police departments”
- Also stated in: § Data collection and standardization. · plain
“Our primary dataset includes 94,778,505 stops from 21 state patrol agencies and 35 municipal police departments.”
§ A large-scale analysis of racial disparities in police stops across the United States > Compiling a national database of traffic stops.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Data collection and standardization. · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the policy change reduced the absolute gap in search rates between race groups; however, the relative gap persisted, with black and Hispanic drivers still more likely to be searched than white drivers post-legalization.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These differences are again large and statistically significant: the 95% credible interval for the Hispanic-white gap is (-8.4,-5.6%); and the analogous interval for the black-white gap is (-6.5%, -3.1%).”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the proportion of stops resulting in a search fell substantially in both Colorado and Washington, while in 12 states where marijuana was not legalized, search rates did not show sharp drops at the end of 2012.”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite marijuana legalization decreasing search rates for white, black and Hispanic drivers, Fig. 4 shows that the relative disparity between white and non-white drivers remains.”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Supplementary Fig. 3 shows that white drivers faced consistently higher search thresholds than black and Hispanic drivers, both before and after marijuana legalization.”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The latent signal distributions for each race and county are homoskedastic discriminant distributions.
- The authors state this as an assumption: The parameters φ and δ can be decomposed into additive race and location terms.
- The authors state this as an assumption: For each stop, a signal is drawn from the race-and-location-specific distribution; a search occurs if the signal exceeds the race-and-location-specific threshold; if searched, contraband is found with probability equal to the signal.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For all three depicted time windows, black drivers comprise a smaller share of stopped drivers after dark, when a veil of darkness masks their race, suggestive of racial profiling.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The proportion of drivers with typically Hispanic names labelled as Hispanic in the raw data was 70% in Colorado and 79% in Arizona, much higher than in Texas.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Despite these lower inferred search thresholds, hit rates for black drivers are comparable to those for white drivers, ostensibly due to the problem of infra-marginality in outcome tests.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Supplementary Fig. 3 also shows that the threshold faced by all groups decreases after legalization (although not all drops are statistically significant).”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that α race r I is large and negative for white, black and Hispanic drivers, suggesting that the observed drop in searches in Colorado and Washington was due to marijuana legalization in those states.”
§ A large-scale analysis of racial disparities in police stops across the United States > Effects of legalization of marijuana.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: For each stop, a signal is drawn from the race-and-location-specific distribution; a search occurs if the signal exceeds the race-and-location-specific threshold; if searched, contraband is found with probability equal to the signal.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The outcome test thus indicates that search decisions may be biased against Hispanic drivers, but the evidence is more ambiguous for black drivers.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Officers exercise discretion in whom to search.
- The authors state this as an assumption: In the absence of discrimination, searched minority drivers should be found with contraband at the same rate as searched white drivers.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“suggesting discrimination among both state patrols and municipal police departments.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- The authors state this as an assumption: Officers who engage in racial profiling are less able to identify a driver's race after dark than during the day.
- Rests on an assumption: Race-specific driving behaviour is not related more to lighting than to time of day.
- Rests on an assumption: Artificial lighting does not substantially weaken the relationship between sunlight and visibility.
- Rests on an assumption: Violation type is not related to lighting in a way that biases the veil-of-darkness test.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The data thus suggest that, although overall search rates dropped in Washington and Colorado, black and Hispanic drivers still faced discrimination in search decisions.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Search policies do not differ within the geographic subdivisions considered.
- Rests on an assumption: Officers do not disproportionately suspect more serious criminal activity when searching black and Hispanic drivers than when searching white drivers.
- Rests on an assumption: The Bayesian threshold model's functional form and prior distributions are adequate for identifying search thresholds.
In the paper
“past work revealed that Texas State Patrol officers incorrectly recorded many Hispanic drivers as white, an error the agency subsequently corrected 36 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
What the sources say
- Not matchedcites [36] Friberg, B. et al. Texas Troopers Ticketing Hispanic Drivers as White (KXAN, 2015); https://www.kxan.com/investigations/texas-troopers-ticketing-hispanic-drivers-as-white/
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We used all available data that were sufficiently complete to conduct our analysis.”
§ A large-scale analysis of racial disparities in police stops across the United States > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The raw data we received from the states and cities, the processed data we used in this analysis and the code to clean and analyse the data are all available for public inspection and reuse.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The threshold test for discrimination was introduced by Simoiu et al. 7 to mitigate the most serious shortcomings of benchmark and outcome analysis.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
What the sources say
- Not matchedcites Simoiu et al. 7
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Under this model of officer behaviour, we interpret lower search thresholds for one group relative to another as evidence of discrimination.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The left panel (Colorado) represents 1,674,619 stops and the right panel (Washington)represents 3,985,677 stops.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“we applied a computationally fast variant of the threshold test developed by Pierson et al. 22 , which we fit separately on both state patrol stops and municipal police stops.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
What the sources say
- Not matchedcites Pierson et al. 22
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we re-categorized as 'Hispanic' all drivers in Texas with Hispanic names who were originally labelled 'white' or who had missing race data; this method adds about 1.9 million stops of Hispanic drivers over the period 2011-2015.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If, for example, officers disproportionately patrol black and Hispanic neighbourhoods, the downstream effects can be injurious even if individual stop decisions are not directly affected by the colour of one's skin.”
§ A large-scale analysis of racial disparities in police stops across the United States > Discussion
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“can place heavy burdens on black and Hispanic drivers without improving public safety 17 .”
§ A large-scale analysis of racial disparities in police stops across the United States > Discussion
What the sources say
- Not matchedcites [17] Chohlas-Wood, A., Goel, S., Shoemaker, A. & Shroff, R. An Analysis of the Metropolitan Nashville Police Department’s Traffic Stop Practices https://policylab.stanford.edu/media/nashville-traffic-stops.pdf (Stanford Computational Policy Lab, 2018).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“since black drivers were more likely to be charged with such offences previous to legalization, these drivers were also disproportionately impacted by the policy change.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We ran numerous automated checks to detect and correct these where possible, although some errors probably remain due to the complex nature of the data.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We did not exclude any data that were deemed sufficient to conduct our analysis.”
§ A large-scale analysis of racial disparities in police stops across the United States > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“the KPT model of behaviour requires strong assumptions, including that drivers and officers are rational actors and that every driver has perfect knowledge of the likelihood that he or she will be searched.”
§ A large-scale analysis of racial disparities in police stops across the United States > Assessing bias in traffic stop decisions.
What the sources say
- Not checked — no full text of the sourcecites [28] Engel, R. S. & Tillyer, R. Searching for equilibrium: the tenuous nature of the outcome test. Justice Q. 25, 54–71 (2008). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Following previous studies 38,39 , we defined a name as 'typically' Hispanic if at least 75% of people with that name identified as Hispanic”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
What the sources say
- Not matchedcites [38] Word, D. & Perkins, C. Building a Spanish Surname List for the 1990s: A New Approach to an Old Problem (Population Division, US Census Bureau, 1996).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [39] Melendres v Arpaio (2009). 598 F. Supp. 2d 1025 (D. Ariz., 2009).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For both the state patrol model and the municipal department model, the prediction errors are minimal and similar across race groups.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The threshold test, however, aims to capture only taste-based discrimination, as is common in the empirical literature on discrimination.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We did not re-categorize drivers in any other jurisdiction.”
§ A large-scale analysis of racial disparities in police stops across the United States > Data collection and standardization.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“although our results are qualitatively similar under alternative averaging schemes.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we limit our analysis to counties and districts with at least 50 searches per race group.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Our complete data-cleaning pipeline is extensive and required subjective decisions”
§ A large-scale analysis of racial disparities in police stops across the United States > Compiling a national database of traffic stops.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The results were statistically significant and qualitatively similar in all cases.”
§ A large-scale analysis of racial disparities in police stops across the United States > Veil-of-darkness analysis.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
Officers exercise discretion in whom to search.
“Implicit in this test is the assumption that officers exercise discretion in whom to search”
The authors state this as an assumption.
In the absence of discrimination, searched minority drivers should be found with contraband at the same rate as searched white drivers.
“Becker argued that even if minority drivers are more likely to carry contraband, in the absence of discrimination, searched minorities should still be found to have contraband at the same rate as searched whites.”
The authors state this as an assumption.
Officers who engage in racial profiling are less able to identify a driver's race after dark than during the day.
“Their method starts from the idea that officers who engage in racial profiling are less able to identify a driver's race after dark than during the day.”
The paper relies on this without support.
Race-specific driving behaviour is not related more to lighting than to time of day.
“Results could be skewed if race-specific driving behaviour is related more to lighting than time of day, leading the test to suggest discrimination where there is none.”
The paper relies on this without support.
Artificial lighting does not substantially weaken the relationship between sunlight and visibility.
“artificial lighting (for example, from street lamps) can weaken the relationship between sunlight and visibility, and so the method may underestimate the extent to which stops are predicated on perceived race.”
The paper relies on this without support.
Violation type is not related to lighting in a way that biases the veil-of-darkness test.
“if violation type is related to lighting, the test could give an inaccurate measure of discrimination.”
The paper relies on this without support.
Search policies do not differ within the geographic subdivisions considered.
“if search policies differ not only across, but also within, the geographic subdivisions we consider, then the threshold test might mistakenly indicate discrimination where there is none.”
The paper relies on this without support.
Officers do not disproportionately suspect more serious criminal activity when searching black and Hispanic drivers than when searching white drivers.
“if officers disproportionately suspect more serious criminal activity when searching black and Hispanic drivers compared to white drivers (for example, possession of larger quantities of contraband), then lower observed thresholds may stem from non-discriminatory police practices.”
The paper relies on this without support.
The Bayesian threshold model's functional form and prior distributions are adequate for identifying search thresholds.
“thresholds cannot be identified by the observed data alone 7 , and so inferences are dependent on the specific functional form of the underlying Bayesian model, including the prior distributions.”
The paper relies on this without support.
Marijuana legalization in Colorado and Washington was not confounded by other simultaneous changes, and parallel trends hold.
“Using a difference-in-differences strategy, we find that legalization reduced both search rates and misdemeanour rates for drug offences for white, black and Hispanic drivers-though a gap in search thresholds persists.”
The authors state this as an assumption.
The latent signal distributions for each race and county are homoskedastic discriminant distributions.
“these signal distributions are modelled as homoskedastic discriminant distributions, a class of logit-normal mixture distributions supported on the unit interval [0, 1].”
The authors state this as an assumption.
The parameters φ and δ can be decomposed into additive race and location terms.
“We impose additional structure on the signal distributions by assuming that ϕ r,d and δ r,d can be decomposed into additive race and location terms.”
The authors state this as an assumption.
For each stop, a signal is drawn from the race-and-location-specific distribution; a search occurs if the signal exceeds the race-and-location-specific threshold; if searched, contraband is found with probability equal to the signal.
“For each stop i, we draw a signal p i from the associated race-and location-specific discriminant distribution.If the signal p i exceeds the race-and location-specific threshold t ri;di I , then a search is conducted and S i = 1; otherwise there is no search and S i = 0.If a search is conducted, contraband is found with probability p i , in which case H i = 1; otherwise, H i = 0.”
The paper relies on this without support.
Recreational marijuana legalization took effect on 9 December 2012 in Washington and 10 December 2012 in Colorado.
“Measures legalizing recreational marijuana took effect on 9 December 2012 in Washington, and on 10 December 2012 in Colorado.”
The paper relies on this without support.
In Washington, probable cause searches are barred because the state does not have an automobile exception for warrantless searches.
“in Washington, we note that probable cause searches are barred because the state does not have an automobile exception for warrantless searches.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These disparities exist even though they cannot be explained by the belief that Black people supposedly commit more crimes, including violent crimes”
From A call to dismantle systemic racism in criminal legal systems. (2022) · cites this paper as Pierson et al., 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Another study, which examined one hundred million traffic stops across the United States, also found that police were less likely to discover contraband or evidence when searching Black and Hispanic drivers than White drivers.”
From The Rise of the Carceral State: Foundations and Contours of a Rapidly Changing Criminal Legal System (2025) · cites this paper as Pierson et al. 2020
The cited source says what the claim attributes to it.
In the citing paper
“They also found that Black drivers were less likely to be stopped by police after sunset-or, put another way, more likely to be stopped during daytime hours when skin color is more readily apparent (Pierson et al. 2020).”
From The Rise of the Carceral State: Foundations and Contours of a Rapidly Changing Criminal Legal System (2025) · cites this paper as Pierson et al. 2020
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Skin tone (i.e., colorism) has long been identified as among the most salient markers for discrimination (10, 11).”
From Nobel and novice: Author prominence affects peer review (2022) · cites this paper as [10]
Experimental — accuracy not yet measuredIn the citing paper
“SROs do not effectively reduce crime in schools (60)”
From Structural Racism and Suicide Prevention for Ethnoracially Minoritized Youth: A Conceptual Framework and Illustration Across Systems (2022) · cites this paper as [60]
Citations
Cites
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- Pierson, E., Corbett-Davies, S. & Goel, S. In International Conference on Artificial Intelligence and Statistics (eds Storkey, A. & Fernando Perez-Cruz, F.) 96–105 (PMLR, 2018).
- Jung, J., Concannon, C., Shroff, R., Goel, S. & Goldstein, D.G. Simple rules to guide expert classifications. J. R. Stat. Soc. Ser. A (in the press).
- Arrow, K. in Discrimination in Labor Markets (eds Ashenfelter, O. & Rees, A.) 3–33 (Princeton Univ. Press, 1973).
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“In fact, there are even recent efforts to use the presence of algorithmic bias as a ‘signal’ of previously unnoticed real ‐ world biases that should potentially be ameliorated (Carton et al., 2016; Pierson et al., 2020).”
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Officer bias, over-patrolling and ethnic disparities in stop and search
Vomfell, L., & Stewart, N. (2021) · Nature Human Behaviour · doi:10.1038/s41562-020-01029-w · Cited by 89
Stelter, M., Essien, I., Sander, C., & Degner, J. (2022) · Psychological Science · doi:10.1177/09567976211051272 · Cited by 75
West, K., Greenland, K., & van Laar, C. (2021) · British Journal of Social Psychology · doi:10.1111/bjso.12458 · Cited by 92
“…but see also Crandall, Ferguson, & Bahns, 2013) and aremotivated to avoid being, appearing to be, or thinking of themselves as prejudiced or discriminatory (Greenland, Xenias, & Maio, 2012; Johns, Cullum, Smith, & Freng, 2008; Plant & Devine, 1998; Schlachter & Rolf, 2017; West & Eaton, 2019).”
BackgroundAdolescent civic engagement: Lessons from Black Lives Matter
Baskin-sommers, A. R., Simmons, C., Conley, M. I., Chang, S.-A. A., Estrada, S., Collins, M. A., Pelham, W. E., Beckford, E., Mitchell-Adams, H., Berrian, N., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109860118 · Cited by 74
“Compared to 24% of White parents, 50% of Black parents reported that thinking about racism and police brutality caused stress for their adolescent, consistent with evidence that Black individuals are more concerned about racial injustice (3), more likely to be subjected to discriminatory practices (4), and experience…”
BackgroundExplanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making
Schoeffer, J., De‐Arteaga, M., & Kühl, N. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642621 · Cited by 105
“…is neither a necessary nor sufficient requirement for fair decision-making [7, 27, 36, 63, 88, 94], and that there exist several real-world examples where the inclusion of sensitive features can make historically disadvantaged groups like Black people or women better off [27, 84, 97, 117].”
BackgroundBarajas, J. M. (2021) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2021.103027 · Cited by 66
Public concerns and connected and automated vehicles: safety, privacy, and data security
Lee, D., & Hess, D. J. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01110-x · Cited by 69
Inflammatory Political Campaigns and Racial Bias in Policing
Grosjean, P. A., Masera, F., & Yousaf, H. (2020) · The Quarterly Journal of Economics · doi:10.1093/qje/qjac037 · Cited by 72
Racial Oppression and Racial Projects in Consumer Markets: A Racial Formation Theory Approach
Crockett, D. (2021) · Journal of Consumer Research · doi:10.1093/jcr/ucab050 · Cited by 63
Wheelock, D., Stroshine, M. S., & O’Hear, M. M. (2018) · Crime & Delinquency · doi:10.1177/0011128718811928 · Cited by 56
Contested Killings: The Mobilizing Effects of Community Contact with Police Violence
MORRIS, K. T., & Shoub, K. (2023) · American Political Science Review · doi:10.1017/s0003055423000321 · Cited by 57
“…to slavery (e.g., Black-mon 2009) and of targeted police violence against Black communities in the United States (e.g., Hinton 2021), to large-n studies of racial disparities in traffic stops (e.g., Baumgartner, Epp, and Shoub 2018; Epp, Maynard-Moody, and Haider-Markel 2014; Pierson et al. 2020).”
BackgroundAvoiding drill-down fallacies with VisPilot
Lee, D. J., Dev, H., Hu, H., Elmeleegy, H., & Parameswaran, A. (2019) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3301275.3302307 · Cited by 49
Monaghan, J. (2023) · doi:10.1093/oso/9780197610725.001.0001 · Cited by 53
A Critical Survey on Fairness Benefits of Explainable AI
Deck, L., Schoeffer, J., De‐Arteaga, M., & Kühl, N. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658990 · Cited by 107
“…sensitive features, such as gender or race, may significantly improve performance for historically marginalized groups like Black people and women and reduce algorithmic bias [114, 131, 139, 164, 196]; and this is closely connected to the presence of differential sub-group validity [105].”
BackgroundEffects of environmental stressors on daily governance
Obradovich, N., Tingley, D. H., & Rahwan, I. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803765115 · Cited by 49
“These data increase in size over time due to amplified rates of police department stop reporting (23).”
BackgroundAmemiya, J. L., Mortenson, E., Heyman, G. D., & Walker, C. M. (2022) · Perspectives on Psychological Science · doi:10.1177/17456916221093593 · Cited by 63
Do Female Officers Police Differently? Evidence from Traffic Stops
Shoub, K., Stauffer, K. E., & Song, M. (2021) · American Journal of Political Science · doi:10.1111/ajps.12618 · Cited by 48
Police brutality and racism in America
Schwartz, S. A. (2020) · EXPLORE · doi:10.1016/j.explore.2020.06.010 · Cited by 44
Ekstrom, P. D., Forestier, J. M. L., & Lai, C. K. (2022) · Psychological Science · doi:10.1177/09567976211053573 · Cited by 42
“, 2018) and is unlikely to be attributable to racial differences in traffic-offense rates (Pierson et al., 2020).”
BackgroundMethodResultInfluentialBurt, S. A. (2022) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-072220-015507 · Cited by 40
Fogliato, R., Xiang, A., Lipton, Z. C., Nagin, D. S., & Chouldechova, A. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462538 · Cited by 45
Blankenship, K. M., Rosenberg, A. P., Schlesinger, P., Groves, A. K., & Keene, D. E. (2023) · American Journal of Public Health · doi:10.2105/ajph.2022.307116 · Cited by 41
Njoku, A., Ahmed, Y., & Bolaji, B. (2020) · Journal of Human Behavior in The Social Environment · doi:10.1080/10911359.2020.1822251 · Cited by 33
“searching Black or Hispanic drivers was lower than for searching White drivers (Pierson et al., 2020).”
BackgroundMiller, J., Quinton, P., Alexandrou, B., & Packham, D. (2020) · Criminology & Public Policy · doi:10.1111/1745-9133.12524 · Cited by 37
The effect of differential victim crime reporting on predictive policing systems
Akpinar, N.-J., De‐Arteaga, M., & Chouldechova, A. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445877 · Cited by 41
“Much of the attention has focused on biases in policeinitiated and particularly arrest data, for instance racial bias in drug related arrests or trac stops in the US [6, 40].”
BackgroundThe Devil is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool
Gerchick, M. K., Jegede, T., Shah, T., Gutiérrez, A. B., Beiers, S., Shemtov, N., Xu, K., Samant, A., & Horowitz, A. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594081 · Cited by 41
“can increase an individual’s and household’s AFST score, raising serious concerns that including these racially disparate features — which reflect the racially biased policing and criminal legal systems [4, 9, 60, 80, 83] — could exacerbate and reify existing racial biases.”
BackgroundChoi, H., & Hong, S. (2020) · Public Administration · doi:10.1111/padm.12696 · Cited by 34
Pedestrian crash frequency: Unpacking the effects of contributing factors and racial disparities
Haddad, A. J., Mondal, A., Bhat, C. R., Zhang, A., Liao, M., Macias, L. J., Lee, M. K., & Watkins, S. C. (2023) · Accident Analysis & Prevention · doi:10.1016/j.aap.2023.106954 · Cited by 34
“…course, these are police-reported crimes, and so the high reported crimes in MB CBGs may also, in part, be a result of disproportionately higher arrests of minority individuals for seemingly similar infractions as those engaged in by non-minorities (Baumgartner et al., 2018; Pierson et al., 2020).”
ResultA Geometric Solution to Fair Representations
He, Y., Burghardt, K., & Lerman, K. (2020) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3375627.3375864 · Cited by 43
The use and effectiveness of investigative police stops
Epp, D. A., & Erhardt, M. (2020) · Politics Groups and Identities · doi:10.1080/21565503.2020.1724160 · Cited by 33
Fiscal Pressures and Discriminatory Policing: Evidence from Traffic Stops in Missouri
Harris, A. P., Ash, E., & Fagan, J. A. (2020) · The Journal of Race Ethnicity and Politics · doi:10.1017/rep.2020.10 · Cited by 31
“Finally, there are big differences in stops per person, with Blacks having an especially high number of stops per person on average.”
BackgroundInfluentialUsing High-Frequency Evaluations to Estimate Discrimination: Evidence from Mortgage Loan OfficersPreprint
Giacoletti, M., Heimer, R. Z., & Yu, E. G. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3795547 · Cited by 32
The roots of racialized travel behavior
Barajas, J. M. (2021) · Advances in transport policy and planning · doi:10.1016/bs.atpp.2021.06.007 · Cited by 32
Thorstad, D. (2024) · doi:10.1093/oso/9780198886143.001.0001 · Cited by 36
How Does Structural Racism Operate (in) the Contemporary US Criminal Justice System?
Lee, H. (2023) · Annual Review of Criminology · doi:10.1146/annurev-criminol-022422-015019 · Cited by 35
Deinse, T. B. V., Zielinski, M. J., Holliday, S. B., Rudd, B. N., & Crable, E. L. (2023) · Implementation Science Communications · doi:10.1186/s43058-023-00521-4 · Cited by 33
“3207; [1, 2]).”
Shoub, K., Epp, D. A., Baumgartner, F. R., Christiani, L., & Roach, K. (2020) · The Journal of Race Ethnicity and Politics · doi:10.1017/rep.2020.8 · Cited by 28
“…there is a consistent pattern across agencies and across the nation: the driver’s race strongly affects the outcome (Baumgartner, Epp, and Shoub 2018; Baumgartner et al. 2017; Epp, Maynard-Moody, and Haider-Markel 2014; Gelman, Fagan, and Kiss 2007; Peffley and Hurwitz 2010; Pierson et al. 2020).”
BackgroundInductive Risk, Understanding, and Opaque Machine Learning Models
Sullivan, E. (2022) · Philosophy of Science · doi:10.1017/psa.2022.62 · Cited by 29
“In the data archives Pierson et al. accessed, race was recorded based on the stopping officer ’ s perception.”
MethodCooley, E., Brown-Iannuzzi, J. L., & Cottrell, D. (2019) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2019.103885 · Cited by 29
Effect of racial misclassification in police data on estimates of racial disparities
Laniyonu, A., & Donahue, S. T. (2023) · Criminology · doi:10.1111/1745-9125.12329 · Cited by 30
Shoub, K., Christiani, L., Baumgartner, F. R., Epp, D. A., & Roach, K. (2020) · Policy Studies Journal · doi:10.1111/psj.12412 · Cited by 26
“…racially disparate outcomes—black people are stopped and searched more than white people during street stops (Gelman et al., 2007) and traffic stops (e.g., Epp et al., 2014; Miller, 2009; Moon & Corley, 2007; Pierson et al., 2020; Rojek, Rosenfeld, & Decker, 2004, 2012; Smith & Petrocelli, 2001).”
BackgroundResultA Causal Framework for Observational Studies of Discrimination
Gaebler, J., Cai, W., Basse, G., Shroff, R., Goel, S., & Hill, J. (2022) · Statistics and Public Policy · doi:10.1080/2330443x.2021.2024778 · Cited by 28
“…(e.g., perceived race or perceived gender), which ostensibly can be manipulated—for example, by changing the name listed on an employment application (Bertrand and Mullainathan 2004), or by masking an individual’s appearance (Goldin and Rouse 2000; Grogger and Ridgeway 2006; Pierson et al. 2020).”
BackgroundSelf-citationSmith, N. C. (2021) · Social Science Research · doi:10.1016/j.ssresearch.2021.102593 · Cited by 30
Explainable machine learning for predicting homicide clearance in the United States
Campedelli, G. M. (2022) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2022.101898 · Cited by 26
Nobel and Novice: Author Prominence Affects Peer Review
Huber, J., M. Inoua, S., Kerschbamer, R., König-Kersting, C., Palan, S., & Smith, V. L. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4190976 · Cited by 146
Blind Justice: Algorithmically Masking Race in Charging Decisions
Chohlas-Wood, A., Nudell, J., Yao, K., Lin, Z. (., Nyarko, J., & Goel, S. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462524 · Cited by 22
“For Hispanic results in this paper, we resort to using Census name frequencies [46] to estimate the likelihood that a given individual is Hispanic, but this process makes the label partly unreliable.”
BackgroundMethodSelf-citationMitchell, J., & Chihaya, G. K. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114989 · Cited by 29
How place and race drive the numbers of fatal police shootings in the US: 2015–2020
Zare, H., Meyerson, N. S., Delgado, P., Crifasi, C. K., Spencer, M., Gaskin, D. J., & Thorpe, R. J. (2022) · Preventive Medicine · doi:10.1016/j.ypmed.2022.107132 · Cited by 28
Nix, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236158 · Cited by 25
Latent motives guide structure learning during adaptive social choice
van Baar, J. M., Nassar, M. R., Deng, W., & FeldmanHall, O. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01207-4 · Cited by 26
“For example, police officers may disproportionally target racial minorities 55 because of a bias in information search, where inaccurate mental models of minorities’ motives cause police officers to misinterpret behavior or ignore exonerating information.”
BackgroundSmartphone Data Reveal Neighborhood-Level Racial Disparities in Police Presence
Chen, M. K., Christensen, K. L., John, E., Owens, E. G., & Zhuo, Y. (2023) · The Review of Economics and Statistics · doi:10.1162/rest_a_01370 · Cited by 24
“Our work complements existing spatial analyses of downstream measures of policing, which have found that police engage in more enforcement actions in Black neighborhoods (Geller et al. 2014; Ba et al. 2021; Pierson et al. 2020).”
BackgroundSystemic Discrimination: Theory and Measurement
Bohren, J. A., Hull, P., & Imas, A. (2025) · The Quarterly Journal of Economics · doi:10.1093/qje/qjaf022 · Cited by 28
Smith, M. R., Tillyer, R., Lloyd, C. D., et al. (2019) · Justice Quarterly · doi:10.1080/07418825.2019.1660395 · Cited by 23
“This approach has since been widely replicated in the literature (Channin, Welsh, Nurge, & Henry, 2016; Pierson et al., 2019; Ritter & Bael, 2009; COPS, 2016; Ross, Fazzalaro, Barone, & Kalinowski, 2016; Taniguchi et al., 2016; Worden et al., 2012) as it is does not require external data for a…”
BackgroundResultInfluentialDon’t Be Fooled, Trauma Is a Systemic Problem: Trauma as a Case of Weaponized Educational Innovation
Khasnabis, D., & Goldin, S. (2020) · Occasional Paper Series · doi:10.58295/2375-3668.1353 · Cited by 22
Using High-Frequency Evaluations to Estimate Discrimination: Evidence from Mortgage Loan Officers
Giacoletti, M., Heimer, R. Z., & Yu, E. G. (2021) · Working paper · doi:10.21799/frbp.wp.2021.04 · Cited by 22
“Police officers are less likely at night than during the day to pull over Black motorists because the driver’s race is difficult to identify (Pierson et al., 2020).”
A call to dismantle systemic racism in criminal legal systems.
Najdowski, C. J., & Stevenson, M. C. (2022) · Law and Human Behavior · doi:10.1037/lhb0000510 · Cited by 21
A simple, statistically robust test of discrimination
Gaebler, J., & Goel, S. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2416348122 · Cited by 21
Self-citationBoch, S. J., & Ford, J. L. (2021) · Nursing Research · doi:10.1097/nnr.0000000000000522 · Cited by 14
Addressing Emergency Department Care for Patients Experiencing Incarceration: A Narrative Review
Rachel E. Armstrong, M., Hendershot, K. A., Newton, N. P., & Panakos, P. (2023) · Western Journal of Emergency Medicine · doi:10.5811/westjem.59057 · Cited by 22
Seto, C. H.-F., Schmidgall, D. K., & Perry, S. L. (2024) · Sociological Quarterly · doi:10.1080/00380253.2024.2435445 · Cited by 20
“…Hoekstra and Sloan 2022; Kochel, Wilson, and Mastrofski 2011; Kramer and Remster 2018; Mitchell and Caudy 2015; Morrow, White, and Fradella 2017; Pierson et al. 2020), has contributed to the need for empirical research focused on the sociodemographic and ideological mechanisms underlying…”
Crime-time: how ambient light affects crime
Domínguez, P., & Asahi, K. (2022) · Journal of Economic Geography · doi:10.1093/jeg/lbac011 · Cited by 20
Race in representative bureaucracy theory: A problematizing review
Strader, E., Griffie, V., Irelan, P. C., et al. (2023) · Public Administration Review · doi:10.1111/puar.13640 · Cited by 20
At the intersection: Race, gender, and discretion in police traffic stop outcomes
Roach, K., Baumgartner, F. R., Christiani, L., Epp, D. A., & Shoub, K. (2020) · The Journal of Race Ethnicity and Politics · doi:10.1017/rep.2020.35 · Cited by 21
Camera Point-of-View Exacerbates Racial Bias in Viewers of Police Use of Force Videos
Bailey, R. L., Read, G. L., Yan, H. Y., Liu, J., Makin, D. A., & Willits, D. W. (2021) · Journal of Communication · doi:10.1093/joc/jqab002 · Cited by 19
De-policing, police stops, and crime
Powell, Z. A. (2022) · Policing A Journal of Policy and Practice · doi:10.1093/police/paac070 · Cited by 19
Limantė, A. (2023) · Nordic Journal of Human Rights · doi:10.1080/18918131.2023.2277581 · Cited by 30
Use of Racial and Ethnic Categories in Medical Testing and Diagnosis: Primum Non Nocere
Kaufman, J. S., Merckx, J., & Cooper, R. (2021) · Clinical Chemistry · doi:10.1093/clinchem/hvab164 · Cited by 24
Careers in Criminalization: Reentry, Recidivism, and Repeated Incarceration
Western, B., & Harding, D. J. (2022) · Crime and justice · doi:10.1086/721742 · Cited by 20
“Such police stops are disproportionately experienced in communities of color (Pierson et al. 2020).”
BackgroundDoesn't everybody jaywalk? On codified rules that are seldom followed and selectively punished
Wylie, J., & Gantman, A. P. (2022) · Cognition · doi:10.1016/j.cognition.2022.105323 · Cited by 17
Donahue, S. T. (2023) · American Sociological Review · doi:10.1177/00031224231173070 · Cited by 18
“Benchmarking searches on motor vehicle stops is a relatively common practice (Anwar and Fang 2006; Pierson et al. 2020), but it is not without its own set of limitations.”
BackgroundAll cops are trusted? How context and time shape immigrants’ trust in the police in Europe
Czymara, C. S., & Mitchell, J. (2022) · Ethnic and Racial Studies · doi:10.1080/01419870.2022.2060711 · Cited by 17
“between groups are themselves subject to endogeneity (Pierson et al. 2020), which complicates efforts to understand how discrimination on the basis of ethnicity or immigration status might affect trust (Resh 2018;”
BackgroundWright, J. E., Gaozhao, D., & Snow, M. A. (2020) · Public Performance & Management Review · doi:10.1080/15309576.2020.1798789 · Cited by 17
“Hispanic and Black drivers are also more likely to be ticketed, searched, and arrested than White drivers (Pierson et al., 2017).”
BackgroundPeterson, C., Nelson, T. F., & Pereira, M. A. (2021) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2021.06.016 · Cited by 17
Najdowski, C. J., & Goff, P. A. (2021) · Social Issues and Policy Review · doi:10.1111/sipr.12083 · Cited by 19
Fitch, K. V., Pence, B. W., Rosen, D. L., Miller, V. E., Gaynes, B. N., Swilley-Martinez, M. E., Kavee, A. L., Carey, T. S., Proescholdbell, S., & Ranapurwala, S. I. (2023) · American Journal of Epidemiology · doi:10.1093/aje/kwad214 · Cited by 19
Discretion in Traffic Stops: The Influence of Budget Cuts on Traffic Citations
Su, M. (2020) · Public Administration Review · doi:10.1111/puar.13300 · Cited by 17
Siegel, M., Poulson, M. R., Sangar, R., & Jay, J. S. (2021) · Race and Social Problems · doi:10.1007/s12552-020-09307-y · Cited by 23
Actively Addressing Systemic Racism Using a Behavioral Community Approach
Watson‐Thompson, J., Hassaballa, R., Valentini, S. H., Schulz, J. A., Vanchy, P., Harsin, J. D., Thompson, V. M., Hassaballa, I., Esiaka, C., & Thompson, E. C. (2022) · Behavior and Social Issues · doi:10.1007/s42822-022-00101-6 · Cited by 25
“It was found that law enforcement conduct traffic stops more routinely and conduct searches more frequently for Black drivers as compared to White or Hispanic drivers (Pierson et al., 2020).”
BackgroundSelf-control in first grade predicts success in the transition to adulthood
Johnson, S. B., Voegtline, K., Ialongo, N. S., Hill, K. G., & Musci, R. J. (2022) · Development and Psychopathology · doi:10.1017/s0954579421001255 · Cited by 25
“Prior research demonstrates that among young adults of color in urban environments, contact with police is common; people of color aremore likely to be stopped by police than their white peers for the same or lesser infractions (Pierson et al., 2020).”
BackgroundDetecting disparities in police deployments using dashcam data
Franchi, M., Zamfirescu-Pereira, J. D., Ju, W. G., & Pierson, E. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594020 · Cited by 17
“Work has also studied post-stop outcomes: for example, [29] studies racial disparities in whether police grant leniency to drivers pulled over for speeding; [2, 36, 52, 53, 64] study racial disparities in police searches; and [73] studies racial disparities in how respectful officers are to drivers of different races.”
BackgroundInfluentialSelf-citationWhistle the Racist Dogs: Political Campaigns and Police StopsPreprint
Grosjean, P. A., Masera, F., & Yousaf, H. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3662027 · Cited by 14
Health, safety, and socioeconomic impacts of cannabis liberalization laws: An evidence and gap map
Sevigny, E. L., Greathouse, J., & Medhin, D. N. (2023) · Campbell Systematic Reviews · doi:10.1002/cl2.1362 · Cited by 15
Boehme, H. M., & Mourtgos, S. M. (2024) · Criminology & Public Policy · doi:10.1111/1745-9133.12673 · Cited by 16
Breaking Taboos in Fair Machine Learning: An Experimental Study
Nyarko, J., Goel, S., & Sommers, R. (2021) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3465416.3483291 · Cited by 15
Self-citationIngber, A. S., Prena, K., & Cummings, J. J. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107188 · Cited by 18
Predicting and explaining with machine learning models: Social science as a touchstone
Buchholz, O., & Grote, T. (2023) · Studies in History and Philosophy of Science · doi:10.1016/j.shpsa.2023.10.004 · Cited by 15
No Justice, No Peace: Political Science Perspectives on the American Carceral State
Harris, A. P., Walker, H. L., & Eckhouse, L. (2020) · The Journal of Race, Ethnicity, and Politics · doi:10.1017/rep.2020.21 · Cited by 15
Racial bias and DUI enforcement: Comparing conviction rates with frequency of behavior
Kagawa, R. M. C., McCort, C. D., Schleimer, J. P., Pear, V. A., Charbonneau, A., Buggs, S., Wintemute, G. J., & Laqueur, H. S. (2021) · Criminology & Public Policy · doi:10.1111/1745-9133.12558 · Cited by 18
Na, C., Lee, S., & Kang, I. (2023) · Policing A Journal of Policy and Practice · doi:10.1093/police/paad025 · Cited by 19
Hannon, L., Neal, M., & Gustafson, A. R. (2020) · Crime & Delinquency · doi:10.1177/0011128720926122 · Cited by 15
Kang, I. (2023) · Journal of policy analysis and management · doi:10.1002/pam.22493 · Cited by 9
Metadata
- Authors
- Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Jenson, D., Shoemaker, A., Ramachandran, V., Barghouty, P., Phillips, C., Shroff, R., & Goel, S.
- Affiliations
- Stanford University (US)
- New York University (US)
- Corresponding author
- Goel, Sharad
- Venue
- Nature Human Behaviour
- Published
- 2020-05-04
- Volume
- 4
- Issue
- 7
- Pages
- 736-745
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W2705765409
- Cited by
- 552
- References
- 42
- Citations by year
- 2026: 59 · 2025: 74 · 2024: 97 · 2023: 109 · 2022: 79 · 2021: 71 · 2020: 34
- FWCI
- 254.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- John S. and James L. Knight Foundation
- Hellman Foundation
- Topics
- Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Education › Education Discipline and Inequality
- Keywords
- racial disparities in policing, traffic stops, police searches, racial bias, Black drivers, recreational cannabis legalization, contraband, racial profiling, United States
- MeSH terms
- Automobile Driving, Black or African American, Female, Hispanic or Latino, Humans, Male, Time Factors, United States, Police, White People, Racism
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-29, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-29 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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