AI Risk Scoring for Recidivism and Reintegration Services
What it is for
- Identifies newly admitted individuals at high risk of recidivism within 12 months of discharge so they can be offered targeted reintegration services, including case management and community resource connections. The tool does not make custody or sentencing decisions.
NYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- Run by
- Department of Correction (DOC)
- Where
- No fixed location
- Kept
- Not stated by the NYC Office of Technology and Innovation.
- Shared with
- Accountable organization, Not available to vendor
What it is for
This tool is used by the NYC Department of Correction to classify newly admitted individuals as either high or low risk of reoffending within 12 months of release. Those identified as high risk are offered targeted support services — including case management and connections to community resources — funded by a Department of Justice grant. The tool runs approximately weekly and is used by program staff to allocate services; it does not make automatic decisions about custody or sentencing.
What it collects and what happens to it
Data taken in
- Prior admissions to DOC custody, age of first admission, self-reported homelessness, self-reported drug or alcohol misuse, and whether the top charge is a misdemeanor. These inputs relate to identifiable individuals currently in custody.
NYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- History of prior admissions to DOC custody and age of first admission — behavioral and criminal justice history data linked to the individual being assessed.
NYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
Processing
- A binary classification model trained on DOC discharge data from 2020 and 2021. It predicts likelihood of recidivism within 12 months using five input features: prior admissions, self-reported homelessness, self-reported drug or alcohol misuse, top charge type, and age at first admission.
NYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
What it does
- The tool classifies individuals into high or low recidivism risk categories approximately weekly. Program staff then decide which individuals to approach for service enrollment — the AI output is informative and advisory; no automatic action follows.
NYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
Outputs
- A binary classification label — 'high' or 'low' risk of recidivism — assigned to each newly admitted individual. This output is used by program staff to prioritize who receives targeted discharge planning services; it is advisory, not binding.
NYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
Run by
- The NYC Department of Correction deploys and operates this tool through its Division of Programs and Community Partnerships to identify high-risk individuals and connect them with reintegration services.
NYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
Built by
Not stated by the NYC Office of Technology and Innovation.
Kept for
Not stated by the NYC Office of Technology and Innovation.
Shared with
- The risk classification output is available to program staff within the NYC Department of Correction's Division of Programs and Community Partnerships, specifically the Discharge Planners funded by the DOJ grant, who use it to prioritize service delivery.
NYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- The register entry states there is no vendor involved in this tool. The tool appears to be developed and operated internally by the NYC Department of Correction.
NYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
Stored
Not stated by the NYC Office of Technology and Innovation.
How to read the colours
Can it identify you?
- Anonymized data
- Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
- Pseudonymous data
- Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
- Identifiable data
- The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.
Who completes the loop?
- Human decides
- This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
- Human executes
- This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
- Autonomous
- This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.
Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism ToolNYC Department of Correction, Division of Programs and Community Partnerships, 2025.
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- AI registerNYC Algorithmic Tools Compliance Report — Risk of Recidivism Tool
- Register entryPublished by the NYC Office of Technology and Innovation. Reference f12d8c22. This disclosure was drafted with AI assistance.Schema: ai@2026-05-06-beta
What you can do
Ask about this system
Questions go to the NYC Office of Technology and Innovation, not the vendor.
Your rights
- Right to Algorithmic TransparencyIndividuals subject to this tool's assessment may request information about how the risk classification system works through the NYC Department of Correction. The tool is disclosed in the NYC Algorithmic Tools Compliance Report, which is publicly available at the source URL.
- Right to Non-discriminationIndividuals have the right not to be discriminated against on the basis of protected characteristics. Given that the tool uses factors that may correlate with race or socioeconomic status, individuals who believe they have been unfairly classified may raise concerns with the NYC Department of Correction. No specific redress mechanism is described in the register entry.
Risks and safeguards
- Reputational harmIndividuals labelled 'high risk' may be stigmatized or have that label used beyond its intended scope of service allocation.Safeguard: The tool is explicitly described as informative and advisory; program staff determine service enrollment. The output is used to offer additional support, not to restrict liberty or impose penalties. The label is internal to program operations.
- Civil liberties harmRecidivism risk tools have a documented history of encoding racial and socioeconomic disparities, potentially infringing on equal protection and due process. The use of factors such as prior admissions and homelessness status can proxy for protected characteristics.Safeguard: The tool is restricted to resource allocation for voluntary services; it does not affect custodial decisions or sentencing. No independent bias audit or fairness evaluation is mentioned in the register entry.