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NYC Office of Technology and Innovation

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AI Risk Scoring for Family Homelessness Prevention

What it is for

Eligibility & Public Benefits
  • The model identifies families receiving ACS prevention services who are most likely to apply for a homeless shelter, so that limited housing vouchers (100 total) can be prioritized toward those at greatest risk of homelessness.

NYC Algorithmic Tools Compliance Report — Housing Prioritization

Risk Assessment & Triage
  • The model estimates a risk score and produces a rank-ordered list of prevention cases by likelihood of the family applying for homeless shelter within 12 months of starting a prevention service.

NYC Algorithmic Tools Compliance Report — Housing Prioritization

Run by
Administration for Children's Services (ACS)
Where
No fixed location
Kept
Not stated by the NYC Office of Technology and Innovation.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Eligibility & Public BenefitsRisk Assessment & Triage

This system predicts the likelihood that a family receiving child-welfare prevention services will apply for a homeless shelter within the next 12 months. Each month it produces a ranked list of open cases, helping the Administration for Children's Services identify which families are most at risk and should be prioritized for limited housing vouchers. A service provider still meets with each family in person and makes the final determination — the score is one input among several, not a binding decision.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • ACS administrative data about prior and current child-welfare involvement at the start of a prevention case, including New York State Central Registry investigations and time spent in foster care. No identifying information is collected or disclosed.

NYC Algorithmic Tools Compliance Report — Housing Prioritization

Processing

Classification & Prediction
  • A machine learning model trained on ACS historical administrative data (2014–2020, 140,242 training observations) estimates the probability that a family will apply for homeless shelter within 12 months, then ranks all open prevention cases by that probability monthly.

NYC Algorithmic Tools Compliance Report — Housing Prioritization

What it does

Deciding (Analytical AI)
Human decides
  • The model predicts and ranks families by shelter-application risk (a classification and ranking task on structured administrative data). The score is informative only — a service provider conducts a qualitative assessment and makes the final determination on voucher allocation.

NYC Algorithmic Tools Compliance Report — Housing Prioritization

Outputs

A recommendation or prediction
Anonymized data
  • A rank-ordered list of open prevention cases involving children whose families have the highest likelihood of applying for a homeless shelter within 12 months of starting prevention services. The list is produced monthly and used as one of several inputs by service providers.

NYC Algorithmic Tools Compliance Report — Housing Prioritization

Run by

Administration for Children's Services (ACS)
  • The Administration for Children's Services (ACS), Division of Policy, Planning, and Analysis, developed and operates this model to prioritize housing assistance vouchers for families in prevention services.

NYC Algorithmic Tools Compliance Report — Housing Prioritization

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

Available to the accountable organization
  • The ranked list of prevention cases is available to ACS staff and contracted service providers who work with families in prevention services. No identifying information is collected or disclosed beyond ACS administrative systems.

NYC Algorithmic Tools Compliance Report — Housing Prioritization

Not available to me
  • Individual families are not informed of their specific risk score or ranking position. The register states no identifying information is collected or disclosed, and affected families have no direct access to their own model output.

NYC Algorithmic Tools Compliance Report — Housing Prioritization

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 Algorithmic TransparencyFamilies in ACS prevention services may contact ACS to learn that a machine-learning model is used as one factor in identifying candidates for housing vouchers. The NYC Algorithmic Tools Compliance Report (publicly available at the source URL) discloses the model's purpose, inputs, methodology, and limitations.
  • Right to a Human ReviewEvery family identified by the model is met by a service provider who conducts a qualitative assessment of housing needs before any voucher is offered. No automated decision is issued — human judgment is built into every placement.

Risks and safeguards

  • Financial & business harmFamilies with the highest need for housing vouchers may be under-ranked if historical data contains biases (e.g. differential child-welfare system contact rates by race or income).Safeguard: The score is informative only; a service provider conducts a qualitative assessment with each family before any voucher decision is made. The model is one of several identification pathways, reducing reliance on any single algorithm.
  • Loss of autonomyFamilies not surfaced by the model may miss voucher opportunities despite genuine need, effectively limiting their access to housing assistance without knowing a model influenced the outcome.Safeguard: ACS uses this as one of multiple identification pathways; service providers also conduct independent qualitative assessments. The register states that no identifying information is disclosed to or from the model.