All technologies
Every technology the NYC Office of Technology and Innovation has published on this register. Narrow it by what it is for or whether it can identify you.
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.
Showing 13 of 86 published
  AI Risk Scoring for Child Welfare Investigations Risk Assessment & Triage | Administration for Children's Services (ACS) | Pseudonymous data | No fixed location |
AI Risk Scoring for Child Welfare Prevention Cases Risk Assessment & Triage | Administration for Children's Services (ACS) | Anonymized data | No fixed location |
AI Risk Scoring for Child Welfare Prevention Services Risk Assessment & Triage · Planning & Decision-making | Administration for Children's Services (ACS) | Anonymized data | No fixed location |
AI Risk Scoring for Child Welfare Prevention Services Risk Assessment & Triage · Eligibility & Public Benefits | Administration for Children's Services (ACS) | Pseudonymous data | No fixed location |
 AI Risk Scoring for Family Homelessness Prevention Eligibility & Public Benefits · Risk Assessment & Triage | Administration for Children's Services (ACS) | Anonymized data | No fixed location |
AI Risk Scoring for Fire Inspection Prioritization Risk Assessment & Triage · Safety & Security | Fire Department (FDNY) | Anonymized data | No fixed location |
AI Risk Scoring for Homelessness Prevention Services Eligibility & Public Benefits · Risk Assessment & Triage | Department of Social Services (DSS) | Identifiable data | No fixed location |
AI Risk Scoring for Recidivism and Reintegration Services Risk Assessment & Triage | Department of Correction (DOC) | Identifiable data | No fixed location |
AI Risk Scoring for Tenant Harassment Prevention Outreach Risk Assessment & Triage · Eligibility & Public Benefits | Department of Social Services (DSS) | Anonymized data | No fixed location |
AI-Assisted Foodborne Illness Detection from Social Media Risk Assessment & Triage · Healthcare | Department of Health and Mental Hygiene (DOHMH) | Identifiable data | No fixed location |
 AI-Assisted Forensic DNA Mixture Interpretation Enforcement · Risk Assessment & Triage | Office of Chief Medical Examiner (OCME) | Identifiable data | No fixed location |
AI-Assisted Medicaid Lien Identification for Injury Cases Risk Assessment & Triage · Eligibility & Public Benefits | Department of Social Services (DSS) | Identifiable data | No fixed location |
 AI-Powered Cybersecurity Threat Detection for City Infrastructure Safety & Security · Risk Assessment & Triage | Department of Environmental Protection (DEP) | Pseudonymous data | No fixed location |