Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets
Abstract
In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender’s “future dangerousness.” Such algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we use counterfactual reasoning to consider the prospects for improved fairness when members of a disadvantaged class are treated by a risk algorithm as if they are members of an advantaged class. We combine a machine learning classifier trained in a novel manner with an optimal transport adjustment for the relevant joint probability distributions, which together provide a constructive response to claims of bias-in-bias-out. A key distinction is made between fairness claims that are empirically testable and fairness claims that are not. We then use confusion tables and conformal prediction sets to evaluate achieved fairness for estimated risk. Our data are a random sample of 300,000 offenders at their arraignments for a large metropolitan area in the United States during which decisions to release or detain are made. We show that substantial improvement in fairness can be achieved consistently with a Pareto improvement for legally protected classes.
Ingestion failed: Traceback (most recent call last): File "/srv/app/app/worker.py", line 85, in run_once process_job(db, job) File "/srv/app/app/worker.py", line 39, in process_job pdf_path, info = fetch_pdf_temp(candidate["url"]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/srv/app/app/downloader.py", line 129, in fetch_pdf_temp raise ValueError(f"PDF source returned HTTP {response.status_code}.") ValueError: PDF source returned HTTP 403.
No local PDF is available.
GROBID Extracted text; discontinued.
This text is generated from TEI extraction for accessibility, search, and TTS. Formulas, tables, figures, page layout, and references may not perfectly match the original PDF.
No accessible text representation is available. The text extraction service has been discontinued for the time being. If you require this service, for accessibility or any other reason, please submit an issue/request on this page.
Metadata
Issues
No public issues have been filed for this DOI.
Submit an issue
Record history
| When | Event | Field | Old | New |
|---|---|---|---|---|
| 2026-06-18 19:37:53.011249+00:00 | identifier_assigned | DSEID | DSEID-001-6877287 |