SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Does the dataset relate to people? Yes, the raw case records relate to defendants. However, directly identifiable information such as names, addresses, date of birth, case numbers etc has been removed in the curated dataset.
Did you collect the data from the individuals directly, or obtain it via third parties? We obtained the raw case records from a third party, WCCA (https://wcca.wicourts.gov).
Were the individuals notified about the data collection? The authors are not aware of it. However, presumably, the defendants were aware that the information about their cases (and hence the related information about them) is kept in court records and is public information under Wisconsin state laws, when exceptions do not apply.
Did the individuals in question consent to the collection and use of their data? The authors are not aware of it. The information is available publicly under Wisconsin state laws.
If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? WCCA provides option in certain limited cases to petition to have case-records removed. Please see https://wcca.wicourts.gov/faq.html.
For the curated dataset, directly identifiable information such as names, addresses, date of birth, case numbers etc has already been removed.
Has analysis of the potential impact of the dataset and its use on data subjects been conducted? Authors are not aware if Wisconsin state or WCCA had done any such analysis.
For the curated dataset, we have thought carefully about it and redacted all the information that, according to us, could potentially affect subjects.
F.4 Pre-processing and Cleaning
Was any preprocessing of the data done? The accompanying paper describes the details of curating the dataset from the raw case records.
Was the "raw" data saved in addition to the cleaned data? Yes, we saved the data on our institute's secure servers (until our research requires). We do not plan to make this data available to others.
Is the software used to clean the data available? We didn't use any 'data cleaning software'. We have described the steps taken in curating the dataset in the accompanying paper. If useful, we can also provide SQL commands for these steps.
F.5 Uses
Has the dataset been used for any tasks already? The curated dataset that we release has only been used for academic research. We are not aware who else has used the raw case records from WCCA and for which tasks.
Is there a repository that links to any or all papers that use the dataset? Not to our knowledge.
What (other) tasks could the dataset be used for? The dataset is for academic research.
Is there anything about the composition of the dataset or the way it was collected and cleaned that might impact future uses? We have listed limitations of the data in Section 5.2 and in earlier parts of this datasheet (for example, see Section F.3).
Are there tasks for which the dataset should not be used? The dataset should not be used for purposes other than academic research.
F.6 Distribution
Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? Yes, public data.
How will the dataset be distributed? The dataset is hosted at http://clezdata.github.io/wc1d/. Downloads are subject to research only use acknowledgement. In case of any difficulties in accessing the data in the future, interested readers can contact the authors.
When will the dataset be distributed? The dataset is hosted at http://clezdata.github.io/wcld/.
Will the dataset be distributed under a copyright, other IP license, or terms of use? The dataset is distributed under the Creative Commons 4.0 BY-NC-SA license.
Do any export controls or other regulatory restrictions apply to the data? No.
F.7 Maintenance
Who is supporting/hosting/maintaining the dataset? Elliott Ash.
How can the data owner/curator be contacted? Through email: elliott.ash@gess.ethz.ch
Is there an erratum? Not at the time of publishing this paper.
Will the dataset be updated? The existing entries in the dataset are unlikely to be modified. New information may be added.
If the dataset relates to people, are there applicable limits on the retention of data associated with the instances? Public information under applicable law.
Will older versions of the dataset continue to be supported/hosted/maintained? In the unlikely event that the entries in the dataset are to be modified, older version will also be made available, for example, using a version control system.
If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? Individuals interested in contributing are encouraged to contact Elliott Ash at elliott.ash@gess.ethz.ch.
G Additional Information
Table 5 shows the mappings from charge severity categories on WCCA and the numerical ranking that we assigned to these categories. Values 1-6 were assigned for forfeiture charges and hence, not shown in the table. Table 6 provides summary statistics for the column charge severity.
Table 5: Mapping from Charge Severity to Numerical Ranking
Charge	highest charge severity
Felony A	21
Felony B	20
Felony BC	19
Felony C	18
Felony D	17
Felony E	16
Felony F	15
Felony G	14
Felony H	13
Felony I	12
Felony U	11
Misdemeanor A	10
Misdemeanor B	9
Misdemeanor C	8
Misdemeanor U	7
Table 6: Highest Charge Severity in the Dataset
highest charge severity	Count	Percentage
7	516004	34.94
10	460898	31.21
9	145750	9.87
13	116008	7.85
12	74062	5.01
15	37580	2.54
14	30561	2.07
18	26678	1.81
11	21893	1.48
16	21217	1.44
17	17088	1.16
20	6187	0.42
19	1581	0.11
21	845	0.06
8	615	0.04
(a) Missing Recidivism Outcome
(b) Not-Missing Recidivism Outcome
Figure 2: Differences in the distribution of type of offense depending on the sentence cutoff length in recidivism variables.
Table 7 shows results of the machine learning classifiers for the recidivism variable with 2 years sentence cut-off.
Table 7: Recidivism (2 year Sentence Cut-Off) Prediction with  ${95}\%$  Confidence Intervals
Overall	Caucasian	African American
XGBoost
Accuracy	0.6589 ± 0.0016	0.6652 ± 0.0019	0.6460 ± 0.0038
AUC	0.7018 ± 0.0018	0.7026 ± 0.0021	0.7005 ± 0.0034
FPR	0.2177 ± 0.0029	0.2094 ± 0.0029	0.2353 ± 0.0046
FNR	0.5136 ± 0.0043	0.5232 ± 0.0049	0.4970 ± 0.0064
PR	0.3298 ± 0.0030	0.3163 ± 0.0032	0.3567 ± 0.0039
Logistic Regression
Accuracy	0.6457 ± 0.0014	0.6555 ± 0.0016	0.6215 ± 0.0029
AUC	0.6784 ± 0.0015	0.6794 ± 0.0019	0.6760 ± 0.0034
FPR	0.1478 ± 0.0022	0.1430 ± 0.0023	0.1571 ± 0.0026
FNR	0.6429 ± 0.0024	0.6469 ± 0.0030	0.6453 ± 0.0047
PR	0.2351 ± 0.0018	0.2270 ± 0.0020	0.2467 ± 0.0028
Hispanic	Native American	Asian
XGBoost
Accuracy	0.6576 ± 0.0054	0.6273 ± 0.0064	0.6733 ± 0.0149
AUC	0.6724 ± 0.0056	0.6852± 0.0066	0.7016 ± 0.0182
FPR	0.1997 ± 0.0079	0.3223 ± 0.0123	0.2153 ± 0.0130
FNR	0.5720 ± 0.0097	0.4125 ± 0.0105	0.5140 ± 0.0245
PR	0.2872 ± 0.0076	0.4706 ± 0.0091	0.3163 ± 0.0116
Logistic Regression
Accuracy	0.6540 ± 0.0054	0.5988 ± 0.0050	0.6728 ± 0.0152
AUC	0.6533 ± 0.0061	0.6699 ± 0.0070	0.6878 ± 0.0176
FPR	0.1293 ± 0.0058	0.2358 ± 0.0090	0.1339 ± 0.0113
FNR	0.6948 ± 0.0087	0.5316 ± 0.0082	0.6521 ± 0.0222
PR	0.1967 ± 0.0055	0.3658 ± 0.0069	0.2138 ± 0.0096
H Violent/Non-Violent Labels for Charge Descriptions using GPT-4
The prompt used for GPT was as follows:
"In the FBI's Uniform Crime Reporting (UCR) Program, violent crime is composed of four offenses: murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault. Violent crimes are defined in the UCR Program as those offenses which involve force or threat of force.
I will next provide you a list of charge descriptions. For each charge description in the list, I want you to provide me a single word answer (Violent/Non-Violent), depending on whether that charge description refers to a violent crime or not. Before providing the answer, I want you to provide an explanation or thought process of how you go from charge description to the answer. The format of your response should be Charge Description; Thought Process; Violent/Non-Violent. Do not include any other text in your response. The charge description in your response should be exactly same as the charge description in my list (do not correct the formatting or spellings etc in charge descriptions) and in the same order as my list.
Here is the list:
We appended charge descriptions (50 at a time) to the prompt and repeatedly prompted the model until we obtained labels for all the charge descriptions. The model used was 'gpt-4' as on 20-Sep-2023. We set the system message as 'You are a helpful assistant.' The cost of inference was approximately $150, including the costs of trial and error with different prompting and models etc.