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NYC Office of Technology and Innovation

NYC - DTPR AI Register

AI Risk Scoring for Homelessness Prevention Services

What it is for

Eligibility & Public Benefits
  • The RAQ determines whether Homebase applicants qualify for full services (financial assistance, case management) or only brief services, with the goal of directing homelessness prevention resources to households most at risk of entering the shelter system.
Risk Assessment & Triage
  • The tool scores individual applicants to predict their likelihood of entering the DHS shelter system, enabling caseworkers to triage and prioritize those who would benefit most from intensive homelessness prevention services.
Run by
Department of Social Services (DSS)
Where
No fixed location
Kept
Retained Not specified in the register entry
Shared with
Accountable organization, Not available to vendor

What it is for

Eligibility & Public BenefitsRisk Assessment & Triage

This tool scores New York City residents who apply for Homebase homelessness prevention services. Applicants answer questions about their housing situation, employment, and history of homelessness, and their answers are added up into a point score that determines whether they qualify for full services — such as financial assistance and case management — or only brief services. Workers can override the score in limited cases with a supervisor's approval, and the scoring model was developed from historical data on past Homebase enrollees.

What it collects and what happens to it

Data taken in

Sensitive personal information
Identifiable data
  • Applicant personal characteristics including age and pregnancy status, educational attainment, and employment status are collected as inputs to the scoring questionnaire.
About behaviour
Identifiable data
  • Housing history and behaviour including eviction history, household discord, a move in the past year, shelter history, and past and recent experience of homelessness are used as inputs to the risk score.

Processing

Classification & Prediction
  • The RAQ uses a points-based scoring model derived from logistic regression analysis of historical Homebase enrollee data. Each answer to a screening question is assigned a point value; applicants who reach a threshold score are classified as eligible for full Homebase services.

What it does

Deciding (Analytical AI)
Human decides
  • The system scores and classifies applicants into eligibility tiers. Workers are able to override a limited number of model decisions with permission of a supervisor, meaning a human is in the loop on consequential determinations.

Outputs

A decision about you
Identifiable data
  • The tool produces a score that is used to assess eligibility for full versus brief Homebase services — a consequential determination affecting access to financial assistance and case management for households at risk of homelessness.

Run by

Department of Social Services (DSS)
  • The NYC Department of Social Services (DSS), through its Human Resources Administration Homebase program, deploys and operates this risk-scoring tool to determine eligibility for homelessness prevention services. The tool has been in use since June 2012 and was updated using internal DSS research in 2023.

NYC Algorithmic Tools Compliance Report — Homebase Risk Assessment Questionnaire (RAQ)

Built by

Not stated by the NYC Office of Technology and Innovation.

Kept for

Retained Not specified in the register entry
  • The register confirms that identifying information is handled in training data, input data, and output data, but does not specify a retention period. Training data covered Homebase enrollees from 2004 to 2016.
  • Duration: Not specified in the register entry

Shared with

Available to the accountable organization
  • Scores and applicant responses are available to the NYC Department of Social Services / Human Resources Administration Homebase workers and supervisors who administer the program.
Not available to vendor
  • There is no external vendor for this tool. The RAQ was developed internally in conjunction with academic researchers and is operated entirely by NYC DSS.

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.

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 a Human ReviewWorkers are able to override a limited number of model decisions with the permission of a supervisor. Applicants who believe their score does not reflect their situation may ask their Homebase caseworker to seek a supervisory review and override. Contact the NYC Department of Social Services / Human Resources Administration for more information.
  • Right to Algorithmic TransparencyInformation about how the Homebase Risk Assessment Questionnaire works — including its scoring logic, the factors it uses, and its compliance status — is publicly reported each year in the NYC Algorithmic Tools Compliance Report, available at the NYC Open Data portal.

Risks and safeguards

  • Civil liberties harmThe scoring model may systematically disadvantage certain groups through the use of proxies such as shelter history, eviction records, or household composition that may correlate with protected characteristics, potentially limiting access to critical anti-poverty services.Safeguard: Workers are able to override a limited number of model decisions with permission of a supervisor, providing a human check on automated determinations. The model was developed and updated with academic and internal researchers. The tool is reported annually under NYC's algorithmic tools compliance framework.
  • Financial & business harmAn incorrect score could deny a household financial assistance and case management at a critical moment, potentially contributing to housing loss and shelter entry — the outcome the system is designed to prevent.Safeguard: Supervisory override provisions allow workers to escalate cases where the score does not reflect the full situation of the applicant. The model was developed in conjunction with academic researchers and updated in 2023 based on more recent data.