Algorithmic Tools Compliance Report

2025

Fire Department

Management, Analysis, and Planning

RBIS (Risk Based Inspection Program); ALARM (A Learning Approach to Risk Modeling)

2019/11

No

Risk management

Scoring

Informative

Daily

Property; Individuals

Civilian fire injuries/fatalities

NA

Not specified

A Learning Approach to Risk Modeling (ALARM) creates risk scores for each building in the city. These scores are used to schedule our Fire Operations building inspections within the inspectable population of buildings in the city (~330,000 Building Identification Numbers), as a part of the Risk-Based Inspection Program (RBIS).

ALARM is a combined approach using machine learning and risk ratios to assess the risk of a building for structural fire ignition (probability) and civilian fire injury/death (impact). The machine learning algorithm takes incident data, housing characteristics, and 311 data and creates a probability of structural fire ignition. This is combined with a civilian injury or death risk ratio for the building which is based on building characteristics, incident data and nearby felony crimes to create a risk score (range is one to nine), with one being highest risk and nine being lowest risk. Buildings are prioritized within each of the nine risk scores according to the residential population in each building.

NA

No identifying information is collected or disclosed

Each month the team uses a five-year incident dataset and reserves 99 percent of the data to train the ignition model and 80 percent of the data to train the impact model.

The ALARM risk score utilizes data from our Fire and Emergency Medical Services dispatch systems, building characteristic data, 311 calls, felony crimes, census data and civilian injury data.

The tool outputs a risk score from one (highest risk) to nine (lowest risk).

None

NA

NA

NA

NA

NA

2025

Mayor's Office

Mayor's Office of Media and Entertainment

Adobe Photoshop

2024/01

No

Information presentation

Matching

Informative

Almost every day

Individuals

Viewers who watch the city’s television channels; some individuals who appear in Mayor’s Office of Media and Entertainment’s television content

NA

https://www.adobe.com/products/photoshop.html

NYC Media uses Adobe Photoshop to edit images. Adobe Photoshop uses generative AI to allow users to edit images without manual work.

NYC Media uses Adobe Photoshop to make slight edits to some images that appear in some content that is produced in-house and broadcast on the city’s television edits. For example, to comply with Federal Communications Commission regulations for non-commercial educational stations, we may use an AI tool to blur a company’s logo on a t-shirt. As another example, we may use the AI tool to add visual interest, for example, to add legs in a picture that is cropped at the waist.

NA

Input data; Output data

According to Adobe’s website, “generative AI Image models were trained on licensed content, such as Adobe Stock, and public domain content where copyright has expired.”

Images copyrighted by the City of New York or licensed to the City of New York pursuant to an agreement that authorizes edits and, if involving images of people, content that is covered by a written consent form.

Visual content that is broadcast on the City’s television stations.

Adobe

Off-the-shelf, Procurement/Paid pilot

Adobe regularly updates the Photoshop software.

NA

NA

NA

2025

Mayor's Office

Mayor's Office of Media and Entertainment

Adobe Premiere Pro

2021/00

No

Information presentation

Data transformation

Monitored

About every day

Individuals

Individuals who watch content on NYC Media's television channels; individuals who speak in content on NYC Media's television channels

NA

https://www.adobe.com/products/premiere.html

NYC Media uses Adobe Premiere Pro to edit video content broadcast on the city’s television stations. Within Adobe Premiere Pro, we use AI-powered tools to help generate closed captions of some video content that is edited in-house prior to broadcast.

We use AI-powered tools to help a human editor generate closed captions of some video content that is edited in-house prior to broadcast on the city’s television channels.

NA

Input data; Output data

According to Adobe’s website, “Speech to Text is powered by a combination of Adobe proprietary technology — including Adobe Sensei machine learning— and third-party technologies.”

Spoken words in video programs.

Closed captions.

Adobe

Off-the-shelf, Procurement/Paid pilot

Adobe provides regular software updates.

NA

NA

NA

2025

Mayor's Office

Mayor’s Office to End Domestic and Gender Based Violence

AI Transcription on Teams

2024/04

No

Information presentation

Data transformation

Monitored

Daily

Individuals

Participants in the meeting might provide their name and business affiliations

NA

Not specified

Microsoft Teams has a built-in feature that uses AI to create a transcript of the meeting.

The tool provided a transcript of a meeting which was then reviewed by Mayor’s Office to End Domestic and Gender Based Violence staff for accuracy. The transcript was then emailed to meeting participants.

NA

Input data; Output data

Training data is proprietary to Microsoft.

The words spoken during a meeting are captured by the tool.

The tool provides text of the words spoken during the meeting.

Microsoft

Off-the-shelf

Microsoft provides this tool as part of Teams.

NA

NA

NA

2025

Mayor's Office

Mayor's Office of Media and Entertainment

AppTek OmniCaption 300 Closed Captioning Appliance

2022/11

No

Information presentation

Data transformation

Fully autonomous

Almost daily

Individuals

People who watch live City Council and mayoral content televised on NYC Gov and other content televised on NYC World; people who appear in the content that is televised

NA

https://www.apptek.ai/

The AppTek OmniCaption 300 closed captioning appliance uses AI-enabled automatic speech recognition to create closed captions of live television content.

The Mayor’s Office of Media and Entertainment uses the AppTek Omni 300 closed captioning appliance to provide closed captioning of live-broadcasted events (e.g., City Council hearings) and content that is cablecast on NYC World.

NA

Input data; Output data

According to AppTek’s website, the “OmniCaption 300 closed captioning appliance was developed for and trained on broadcast news, sports, weather and other programming.”

The input data are words spoken by people during live broadcasts of public hearings, meetings, and events and content on NYC World.

Closed captions that reflect the written text of the input data (spoken words).

AppTek

Off-the-shelf, Procurement/Paid pilot

AppTek provides support for the Omni 300 closed captioning system.

NA

NA

NA

2025

Mayor's Office

Mayor's Office of Equity and Racial Justice, NYC Office of Talent and Workforce Development

Canva

2025/01

Tool was created in CY2025

Information presentation

Data transformation

Informative

Monthly

Individuals

Audience for generated materials includes internal and external stakeholders, partner organizations and the general public

NA

Not specified

Canva is a cloud-based online graphic design platform for creating professional visuals and documents, such as presentations, websites, and similar products.

The purpose for each Mayor's Office that uses Canva AI is listed below.

For the Mayor's Office of Equity and Racial Justice, Canva's AI feature is used to generate visual material for social media communication.

For NYC Office of Talent and Workforce Development, Canva's AI feature is used to receive suggestions for designing graphics for invitations, flyers, and meeting and presentation decks for events and presentations. The NYC Talent team has limited graphic design expertise on staff and the tool enables more streamlined designs for promotional materials.

NA

No identifying information is collected or disclosed

Training data is proprietary to Canva.

Description of image sought.

Editable graphic and text in response to user design.

Canva

Off-the-shelf

Canva provides a free version of their software to the public, which both Mayor's Office's use. Neither hold a contract with Canva.

NA

NA

NA

2025

Mayor's Office

Mayor’s Office to End Domestic and Gender Based Violence, Office of Community Mental Health, Office of Talent and Workforce Development, and Mayor's Office of Nonprofit Services

ChatGPT

2024/03

Yes

Information presentation

Data transformation

Informative

Daily

Individuals

Public audiences, website and social media users, internal and external stakeholders, including groups and individuals

NA

Not specified

ChatGPT is used by Mayor's Offices as a supplemental tool to support a variety of operational, administrative, and communications functions. ChatGPT is an advanced AI language model that can understand and generate human-like text based on the input it receives. The tool assists staff in improving the clarity, accuracy, and accessibility of written materials, conducting background research, and streamlining workflows. Its use is tailored by each Mayor’s Office to meet programmatic needs.

The purpose for each Mayor's Office that uses ChatGPT is listed below.

The Mayor’s Office to End Domestic and Gender Based Violence (ENDGBV) staff have used ChatGPT to complete the following tasks: assist in gathering information for literature reviews for public facing ENDGBV reports, draft and edit public e-mails, and assist in creating potential job interview questions based on content in the job description.

The Office of Community Mental Health (OCMH) uses the tool in WordPress to write alt text for images on their website, making it accessible to people using assistive technology. The alt text for images are written with inclusive, neutral, and bias-free language. OCMH writes alt text for images of actual humans without the use of ChatGPT to avoid bias and non-consensual use of people’s information. ChatGPT is mostly used in this way to describe images with a lot of text.

The Office of Talent and Workforce Development uses ChatGPT monthly to make text content more streamlined, grammatically correct, and concise; some of this text content is shared with the public or external stakeholders.

The Mayor's Office of Nonprofit Service's uses it to edit content for their social media and Capacity Building trainings for nonprofits.

ChatGPT is updated routinely by OpenAI. In addition to model updates, additional offices within the Mayor's Office have begun to use the product in CY2025: the Office of Community Mental Health began use in August 2025, the Office of Talent and Workforce Development began use in January 2025, and the Mayor's Office of Nonprofit Services began use in January 2025.

Input data; Output data

Training data is proprietary to OpenAI.

Text prompts were provided to ChatGPT.

ChatGPT provided text and image responses to the text prompts.

OpenAI

Off-the-shelf

OpenAI created and maintains ChatGPT.

NA

NA

NA

2025

Mayor's Office

Deputy Mayor for Communications

GoTranscript

2024/00

No

Information presentation

Data transformation

Informative

Multiple times a week

Individuals; Other

General public

Mayor's remarks and speeches

https://gotranscript.com/transcription-services

GoTranscript is an audio and video transcription service.

The Mayor's research team submits the audio for the Mayor's remarks and press conferences to the GoTranscript AI transcript service, which returns a first draft. The research team then does a second and third read, correcting any errors and formatting it properly before sending it to be published as a public transcript.

NA

Input data; Output data

Training data is proprietary to the vendor.

Audio recordings of the mayor's remarks.

Draft transcript for the audio or video recordings of the Mayor's remarks.

GoTranscript

Procurement/Paid pilot

Purchase use of services.

NA

NA

NA

2025

Mayor's Office

Civic Engagement Commission

Methodology for Poll Site Language Assistance

2020/11

Yes

Resource allocation

Ranking

Informative

Twice

Individuals

NA

NA

Not specified

Since no dataset is currently available that reliably captures the number of limited English proficient (LEP) registered voters for all program languages, the Civic Engagement Commission (CEC) uses the percentage of LEP citizens of voting age (CVALEP) as a substitute or proxy measure of need. CEC ranks the program-eligible languages in order of magnitude of CVALEP and distributes poll sites to each language based on its ranking (excluding CVALEP persons that speak languages served by the NYC Board of Elections in certain New York City counties). The number of poll sites that will receive services in any given language will depend on each language’s share of the total CVALEP in the population eligible to be served. For example, according to U.S. Census data, approximately 207,926 New Yorkers are CVALEP and speak a language that is served by this program. This proportionality approach allows CEC to balance goals of including diverse language communities as well as fair access to the total number of eligible voters within each language community. The program provides interpreters in program-eligible languages at poll sites based on U.S. Census data showing concentrations of CVALEP individuals who speak these languages and reside around each poll site. For each language, poll sites are chosen in descending order of concentration of CVALEP, until the language’s share is met. This process is repeated for each language, thereby including the poll sites with the highest concentration of CVALEP for each program-eligible language until that language’s share is met, and the total number of poll sites for which resources are allocated is reached. It may be possible, based on analysis of data, to reassign poll sites to languages with greater need; however, each language will receive a minimum of at least one poll site. Models used included the Thiessen polygon method to create a Voronoi diagram to determine CVALEP estimates.

This is a methodology for determining how the CEC will provide interpretation services at poll sites for LEP voters. The methodology explains how the CEC will identify the languages and locations in which interpretation services will be offered during the November 2025 election and beyond. These services supplement the interpretation assistance provided by NYC Board of Elections in several languages. Under the Charter, the CEC can only provide interpretation services in a language if it is a designated citywide language or it is spoken by a greater number of LEP New Yorkers than the lowest ranked designated citywide language and at least one poll site has a significant concentration of speakers of such language with LEP. This methodology ensures service for all languages that are eligible under the Charter.

Data is now based on the more recent census (American Community Survey 2019-2023 five-year estimates).

No identifying information is collected or disclosed

N/A

For citywide estimates, this methodology uses current data from the American Community Survey 2019-2023 five-year estimates. This methodology also uses the American Community Survey Census Tract 2019-2023 five-year Public Use Microdata Samples for poll site level analysis, which tracks resident New Yorkers at the neighborhood level. In addition, the methodology uses data from the Board of Elections on the location of election districts and poll sites.

The algorithm estimates the number of citizens of voting age with Limited English Proficiency for each program-eligible language who could report to each polling site.

None

NA

NA

NA

NA

NA

2025

Mayor's Office

NYC Office of Talent and Workforce Development, Mayor's Office of Nonprofit Services

Microsoft Copilot

2025/01

Tool was created in CY2025

Information presentation

Data transformation

Informative

Weekly

Individuals

Internal and external stakeholders, including the general public (e.g., MONS social media followers and nonprofit partners)

NA

Not specified

Microsoft 365 Copilot is an AI-powered tool that helps with work tasks by responding to user prompts with AI-generated information.

Beginning in January 2025, the NYC Office of Talent and Workforce Development (NYC Talent) team uses Microsoft Copilot for notetaking by using the speech to text feature if the staff member is unable to type in order to multi-task. Other Copilot features like predictive text in email are also periodically used when corresponding with external parties.

Beginning in September 2025, the Mayor's Office of Nonprofit Services (MONS) uses Microsoft Copilot by prompting it to edit content into different formats for their social media and capacity building trainings for nonprofits.

NA

Input data; Output data

Training data is proprietary to the vendor.

For NYC Talent: Text or speech.
For MONS: Training content (based on professional experience) and drafted social media content.

For NYC Talent: Text.
For MONS: Edited training and social media content.

Microsoft

Off-the-shelf

Off-the-shelf product by Microsoft accessed via paid licenses.

NA

NA

NA

2025

Mayor's Office

Civic Engagement Commission

Panelot

2025/12

Tool was created in CY2025

Resource allocation

Ranking

Supervised

Once

Individuals

New Yorkers who applied to participate in The People's Money's Borough Assemblies

NA

https://panelot.org/

Panelot (panel selection by lot) is a not-for-profit system for randomly selecting citizen panels in a way that is representative of the population and fair to volunteers.

The Civic Engagement Commission (CEC) uses Panelot for the selection of participants in the Borough Assembly phase of our annual Participatory Budgeting process, The People's Money. Panelot receives as input the composition of the pool of volunteers, the desired panel size, and quotas meant to ensure a representative outcome (for example, in a panel of 100 people the number of women might be required to be between 47 and 53). Panelot outputs a list of quota-compliant panels, each with an assigned probability. The list of panels and their probabilities are chosen to be leximin optimal: the output first maximizes the selection probability of any volunteer, then maximizes the second lowest selection probability, then maximizes the third lowest, and so on. The CEC contacts applicants from the output lists. If an applicant is no longer available to participate, the CEC contacts backup volunteers from the same list.

NA

Input data

N/A

Input data consists of an anonymized list of applicants, each with a unique ID that allows them to be referenced with the internal CEC list of applicants. The input data only contains information relevant to reaching quotas. The fields are: type of housing, education, language, income, age, and zip code.

Output data consists of a list of potential participants as well as back-ups for some quota categories.

Panelot

Off-the-shelf

Panelot created and maintains the tool. Vendor is pro-bono.

NA

NA

NA

2025

Mayor's Office

Mayor's Office of Civic Engagement

Zencity

2024/07

No

Information presentation

Classification

Informative

3-4 times

Group, organization, or business

NA

NA

Not specified

Zencity is a survey and large language model (LLM) platform to get community feedback. Zencity has AI features that help write content and come up with research questions to mine media sentiment on specific topics.

Civic Engagement holds the city contract with Zencity. Part of this contract is using the platform's AI to help with content writing for additions to webpages/ information distribution and assists with compiling research on public sentiment from specific topics.

NA

No identifying information is collected or disclosed

Zencity is a company with a proprietary model for their LLM. The sentiment analysis pulls from media sources from news to X posts.

Media articles.

Sentiment analysis and survey takeaways made public via reports or press releases.

Zencity

Procurement/Paid pilot, Professional services

The vendor provided a training on how to use the platform and offers additional assistance. Zencity Organic is the AI function.

NA

NA

NA

2025

Mayor's Office

Mayor's Office of Media and Entertainment

Zoom

2020/00

No

Information presentation

Data transformation

Fully autonomous

Occasional use

Individuals

People who participate in rulemaking hearings and webinars; people who read transcripts of those hearings and webinars

NA

https://www.zoom.com/

Zoom is a virtual meeting platform; Zoom has an auto closed-caption function that uses AI.

The Mayor’s Office of Media and Entertainment (MOME) uses Zoom for public hearings on rulemaking and for public webinars. MOME uses Zoom’s auto transcript function and captioning function. (Note: MOME provides American Sign Language and human-typed Communication Access Realtime Translation services as a reasonable accommodation upon request.) If MOME publishes a transcript after the Zoom meeting, a human reviews and corrects it.

NA

Input data; Output data

Training data is proprietary to Zoom. According to Zoom’s website, “Zoom does not use any customer audio, video, chat, screen sharing, attachments, or other communications-like customer content (such as poll results, whiteboard, and reactions) to train Zoom’s or its third-party artificial intelligence models.”

Speech at MOME's rulemaking hearings and agency webinars.

Text in a transcript and captions.

Zoom

Off-the-shelf, Procurement/Paid pilot

Regular updates to the application.

NA

NA

NA

2025

Mayor's Office of Contract Services

Mayor's Office of Contract Services Learning & Development

AI Transcription on Webex

2025/07

Tool was created in CY2025

Information presentation

Data transformation

Fully autonomous

Biweekly

Individuals

Participants might provide their name and business affiliations

NA

Not specified

WebEx has a built-in feature that uses AI to create a transcript of public meetings, which are shared with participants after the meeting.

MOCS uses the transcription for vendor trainings that are hosted on WebEx. It's a public link that is shared with all who register for or attend the training. A link to the recording, including transcription, is shared with those who request it.

NA

Input data; Output data

Training data is proprietary to Cisco.

The words spoken during a meeting are captured by the tool.

The tool provides text of the words spoken during the meeting.

Cisco

Off-the-shelf

Cisco provides this tool as part of Webex.

NA

NA

NA

2025

New York Police Department

NYPD

Facial Recognition Technology

2011/10

No

Data management

Matching

Informative

Not specified

Individuals

General population

NA

Not specified

Facial recognition is a digital technology which may help investigators identify unknown subjects in law enforcement investigations.

Facial recognition is a digital technology that NYPD uses to compare images obtained during investigations with lawfully possessed arrest and parole photos. The tool analyzes an uploaded image, known as a probe image, and searches and compares against the image repository. The purpose of the tool is to enhance law enforcement’s ability to investigate criminal activity as well as identify deceased persons and missing persons. When used in combination with human analysis and additional investigation, facial recognition technology is a valuable tool in solving crimes and increasing public safety.

NA

Input data; Output data

Training data is proprietary to the vendor.

If NYPD investigators obtain a still image depicting a face of an unknown individual during an investigation, the image can be submitted for facial recognition analysis in accordance with NYPD facial recognition policy. Known as a probe image, NYPD facial recognition software compares the image to a controlled and limited group of lawfully obtained photos called the photo repository.

The facial recognition software will generate a pool of possible match candidates for review by trained Facial Identification Section investigators.

DataWorks

Procurement/Paid pilot, Professional services

Software developed and maintained by vendor.

NA

NA

NA

2025

New York Police Department

NYPD

Patternizr

2016/12

Yes

Data management

Matching

Informative

Not specified

Individuals; Property; Geographic space; Other

NA

Crime classification

Not specified

Patternizr aids crime analysis in detection of potential crime patterns.

Patternizr compares features of crimes and finds ones that are similar and may be part of a crime pattern. Analysts will look at the candidate crimes and suggest the formation of crime patterns to a pattern identification module. If a pattern is formed, detectives often consolidate the investigative efforts (e.g., one detective investigates all the crimes in the pattern.) The report filters non-normal trends into a spreadsheet and displays year-over-year counts of crimes that have non-normal trends. The tool requires a human user to evaluate the output data to see if complaints identified as similar are, in fact, connected to a pattern.

Routine maintenance.

Training data; Input data

Separate models were trained for each of three different crime types (burglaries, robberies, and grand larcenies). These crime types have a sufficient corpus of prior manually identified patterns for use as training examples. This corpus consists of approximately 10,000 patterns between 2006 and 2015 from each crime type. A portion of this corpus includes complaint records where the same individual was arrested for multiple crimes of the same type within a span of two days.

The input data is a candidate crime and its features. A complaint describes details of the crime, including the date and time (which can be a range if the precise time of occurrence is unknown), location, crime subcategory, modus operandi, and suspect information. This information is used to calculate the five types of crime-to-crime similarities used as features by Patternizr: location, date-time, categorical, suspect and unstructured text.

Probability that a complaint is connected to a pattern.

None

NA

NA

NA

NA

NA

2025

New York Police Department

NYPD

ShotSpotter

2015/03

Yes

Data management

Classification

Informative

Not specified

Geographic space

NA

NA

Not specified

ShotSpotter provides acoustic gunshot detection to assist with emergency call response.

Provides acoustic gunshot detection to assist with emergency call response. The tool supports patrol operations in alerting units to potential gunfire and enhances investigations involving firearms.

Routine maintenance.

No identifying information is collected or disclosed

Training data is proprietary to the vendor.

Specialized software analyzes audio signals for potential gunshots.

The tool determines the location of the sound source, and once classified as potential gunfire sends the incident to acoustic experts for additional analysis. Notifications are sent for confirmed gunfire. ShotSpotter activations may result in evidence collection that can enhance case investigations. Problematic locations identified through alerts may require additional resource deployment and/or investigations.

ShotSpotter

Procurement/Paid pilot, Professional services

Software developed and maintained by vendor.

NA

NA

NA

2025

NYC Emergency Management

External Affairs

Microsoft Copilot for M365

2025/07

Tool was created in CY2025

Information presentation

Data transformation

Informative

Occasionally

Individuals

General public

NA

Not specified

Microsoft Copilot for M365 is a generative AI tool.

NYCEM used Copilot to simplify preparedness language for public consumption and to improve accessibility using the plain language guidelines.

NA

No identifying information is collected or disclosed

Training data is proprietary to the vendor.

NYCEM staff input preparedness recommendations that had been previously drafted.

The program output simplified preparedness recommendations, taking into account plain language guidelines.

Microsoft

No-cost engagement

Microsoft provides and updates the software and offered office hours to discuss the tool.

NA

NA

NA

2025

NYC Public Schools

Division of Instructional and Information Technology

Algebra Teaching Assistant

2023/05

No

Information presentation

Data transformation

Fully autonomous

Daily

Individuals

Teachers, students

NA

Not specified

The Algebra Teaching Assistant uses the Division of Instructional and Information Technology AI platform to accesses specific algebra-focused content to provide responses to prompts related to algebra.

The tool is used to generate responses to prompts entered by a student or teacher, requesting the generative AI tool to compose a text response to a text input.

NA

No identifying information is collected or disclosed

The large language model (LLM) has been trained exclusively on curriculum from Illustrative Math.

Prompts provided by the users of the system.

The output data for the Teaching Assistant is the response generated by specifically developed LLM using the Illustrative Math curriculum.

Microsoft

No-cost engagement

Microsoft provided technical guidance for their emerging generative AI technology and built some small modules of code for the specific Teaching Assistant use cases.

NA

NA

NA

2025

NYC Public Schools

Division of Instructional and Information Technology

Annual Professional Performance Review Measures of Student Learning (MOSL) Growth Model

2013/09

No

Performance evaluation

Scoring

Supervised

Daily

Individuals

Teachers

NA

https://tools.nycenet.edu/resources/awa-guide/

The growth model uses a variety of student-level (assessment scores, English language learner, disability, and economic disadvantage indicators), classroom-level (e.g. percent students with disabilities), and school-level data (e.g. percent English language learners, percent students with disability, average prior achievement, school type) to estimate/predict a student’s score on one of many possible course-culminating assessments. These predicted scores are either used to identify “peer groups” of students, from which student growth percentiles (SGPs) are determined, or compared to actual scores to determine student credit values. These units (SGPs or credit values) are then weight-averaged to generate an educator-level result - the Measures of Student Learning (MOSL) rating. The MOSL rating is combined with the Measures of Teaching/Leadership Practice (MOTP/MOLP) rating to produce an Overall Rating. Per state law 3012-d, annual ratings “shall be a significant factor in HR decisions.” This is often implemented by making ratings a qualifying/disqualifying element in decision-making concerning employment, tenure, salary, and other professional opportunities.

In accordance with New York State law and New York State Education Department regulations, NYCPS developed and maintains a “growth model” to produce MOSL ratings for use in annual professional performance reviews for teachers and principals. The MOSL ratings are combined with MOTP/MOLP ratings to produce an annual Overall Rating for each eligible educator.

NA

Training data; Input data

The growth model process is employed in both retrospective and prospective ways. In the retrospective version, the results are determined entirely within-sample. In the prospective version, the coefficients of the model are estimated on multiple prior years of data.

The growth model makes use of three types of data: students’ end-of-year assessment scores, enrollment and attendance records that link students to teachers and schools, and historical academic and demographic information used to identify groups of similar students.

The model outputs an estimate of a student’s score on a course-culminating assessment.

Education Analytics

Professional services

Education Analytics provides technical assistance and quality assurance for the growth model.

NA

NA

NA

2025

NYC Public Schools

Division of Instructional and Information Technology

Annual Professional Performance Review Measures of Teaching/Leadership Practice (MOTP/MOLP) Calculation

2013/10

No

Performance evaluation

Scoring

Supervised

Daily

Individuals

Principals, assistant principals, teachers

NA

https://tools.nycenet.edu/resources/awa-guide/

Throughout a school year, evaluators observe teachers/principals multiple times and use a rubric to provide a numerical rating on one or more rubric components. These rubric component scores are then weight-averaged according to collectively bargained rules to produce a Measure of Teaching/Leadership Practice (MOTP/MOLP) Rating. The MOTP/MOLP rating is combined with the Measures of Student Learning (MOSL) rating to produce an Overall Rating for each eligible educator. Per state education law 3012-d, annual ratings “shall be a significant factor in HR decisions.” This is often implemented by making ratings a qualifying/disqualifying element in decision-making concerning employment, tenure, salary, and other professional opportunities.

In accordance with New York State law and New York State Education Department regulations, NYCPS developed and maintains databases and calculation rules to produce MOTP/MOLP ratings for use in annual professional performance reviews for teachers and principals. The MOTP/MOLP ratings are combined with MOSL ratings to produce an annual Overall Rating for each eligible educator.

NA

No identifying information is collected or disclosed

Pilot data prior to program launch was used to inform the weights assigned to various rubric components. However, the weights are ultimately determined via collective bargaining.

Rubric component numerical ratings.

The model outputs a score for teachers and principals.

None

NA

NA

NA

NA

NA

2025

NYC Public Schools

Division of Instructional and Information Technology

Eureka! Chatbot

2023/08

No

Information presentation

Data transformation

Monitored

Daily

Individuals

NYCPS staff and parents

NA

https://www.schools.nyc.gov/learning/digital-learning/applications-and-platforms/supporthub

The Azure Cognitive Services technology and chatbot (internally branded as “Eureka!”) was configured and deployed in August 2023 to be the first response to calls to the NYCPS IT Service Desk. It accesses scripts to handle four common reasons for a user to call or contact the service desk: Password Reset, Create a Ticket, Ticket Status, Request for Information. The chatbot accesses pre-defined scripts to respond to user voice or text input. The user’s request is either serviced, completed and closed by the chatbot, or the user is given the option (at any time) to connect to a live agent.

The tool is used to respond to common IT service desk requests: Password Reset, Create a Ticket, Ticket Status, Request for Information. Users can access the tool by phone, by computer through the NYCPS Support Hub application, and from links from Microsoft Teams and other NYCPS systems, such as TeachHub.

NA

No identifying information is collected or disclosed

Pre-defined scripts designed to respond to four common requests to the IT Service Desk.

A voice call or text-based chat session initiated by a user and responded to by the Eureka! chatbot before being handled by a human Service Desk agent.

The chatbot generates responses to user-entered prompts based on the training data or forwards the inquiry to a human Service Desk agent. Since its launch in August 2022, the chatbot has handled an average of 1,500 calls and 300 web-based inquiries each day. Approximately 30 percent of the voice calls and 10 percent of the web-based queries have been handled completely by Eureka! without being forwarded to a human Service Desk agent.

Nagarro and Microsoft

Professional services

Developed by an IT services vendor (Nagarro) using Microsoft Cognitive services.

NA

NA

NA

2025

NYC Public Schools

Division of Instructional and Information Technology

MySchools - Match

2018/08

No

Resource allocation

Matching

Monitored

Daily

Individuals

Students

NA

https://enrollmentsupport.schools.nyc/app/answers/detail/a_id/3652

The tool utilizes the Gale-Shapley deferred acceptance algorithm to match applicants to schools. This algorithm has been in existence for many years, used internationally for various purposes. It's most common use is in the National Resident Matching Program for medical school students.

Deferred acceptance works as an iterative series of steps: students and programs are tentatively matched in each step, but nothing is finalized until the algorithm terminates (hence the deferred).

1. Each student “proposes” to their first choice;
- Programs assign seats to students one at a time;
- When all seats are filled, programs may reject previously accepted students in favor of new applications from students they prefer (e.g., students with a better lottery number);
- Remaining students are rejected;
2. Students rejected in the last step “propose” to the next choice on their list;
3. The algorithm terminates when all students are matched or have proposed to all the programs they listed.

MySchools is an application used to house online school directories, collect application choices, and run the admissions matching algorithm that is used for all centralized admissions processes (3-K, pre-K, Gifted & Talented, middle school, and high school). The tool encompasses a family-facing portal, a school-facing portal, and an administrative portal.

NA

Input data; Output data

N/A

Student biographical information (e.g., home address, poverty status, home language), student academic information (e.g., course grades, state test scores), and student school records (e.g., sending school).

The algorithm outputs a school match for each student.

Blenderbox

Professional services

We have a five-year contract with the agency Blenderbox who designed the application and implemented the algorithmic matching functionality. The work is meant to transition to be run in-house, by the Division of Instructional and Information Technology (DIIT) within NYCPS, by the end of the contract. The team at DIIT has already begun to takeover maintenance and development of the tool.

NA

NA

NA

2025

NYC Public Schools

Division of Instructional and Information Technology

MySchools - Probability of Acceptance

2024/09

Yes

Information presentation

Scoring

Fully autonomous

Daily

Individuals

Students

NA

https://enrollmentsupport.schools.nyc/app/answers/detail/a_id/3652

This feature, added to MySchools in 2024, determines a probability of acceptance at a specific school for a future high school student. This is calculated and displayed as a student is selecting schools to apply to in the MySchools application.

Information is presented to students and parents to help them decide what high schools to apply to.

As part of a limited pilot program, some eight grade students were notified that they had a high likelihood of acceptance to a high-performing school. This was done to encourage these students who may not be considering those schools to apply to attend high schools at those schools.

Training data; Input data

Model was developed by researchers affiliated with the Massachusetts Institute of Technology (MIT) and trained on information about New York City public high schools. Students will see an icon indicating whether they have a “high,” “medium,” or “low” chance of receiving an offer, based on the applicant’s admissions characteristics like district or borough, grades, priority group, and the school’s admissions method, such as whether the admission is open or screened.

Student high school selections and student records.

Probability of acceptance for the student to a specific high school, indicated as “high”, “medium” or “low.”

Researchers from Massachusetts Institute of Technology (MIT)

Professional services

MIT developed the tool and the Division of Instructional and Information Technology integrated it into the MySchools system.

NA

NA

NA

2025

NYC Public Schools

Division of Instructional and Information Technology

Open Gen AI and Teaching Assistant Tool

2023/05

No

Information presentation

Data transformation

Supervised

Daily

Individuals

Teachers, students

NA

Not specified

The generative AI system using large language models was a system custom-built by the Division of Instructional and Information Technology using advanced Microsoft technologies to create a set of generative AI tools. “Open Gen AI” accesses a large language model (currently OpenAI’s GPT 3.5) to provide responses to a broad range of prompts.

The tool is used to generate responses to prompts entered by a student or teacher, requesting the generative AI tool to compose a text response to a text input.

NA

Input data

ChatGPT training data is proprietary to OpenAI.

Prompts provided by the users of the system.

The output data for the Open Gen AI tool is the response generated by the ChatGPT large language model.

Microsoft

Procurement/Paid pilot

Microsoft provided technical guidance for their emerging generative AI technology and built some small module of code for the specific NYCPS Gen AI and Teaching Assistant use cases.

NA

NA

NA

2025

Office of Chief Medical Examiner

Department of Forensic Biology

STRmix

2017/01

No

Data management

Matching

Informative

Not specified

Individuals; Biological sample

Those suspected of crime, arrested, and put on trial

NA

Not specified

STRmix™ combines sophisticated biological modelling and standard mathematical processes to interpret a wide range of complex DNA profiles. Using well-established statistical methods, the software builds millions of conceptual DNA profiles.

STRmix is a forensic DNA analysis software program that uses a probabilistic genotyping algorithm to interpret complex DNA profiles, such as those from mixed samples that contain DNA from multiple contributors.

STRmix™ models any types of allelic and stutter peak heights as well as drop-in and drop-out behavior.  It does this rapidly, accessing evidential information previously out of reach with traditional methods.  STRmix™ is supported by comprehensive empirical studies with its mathematics readily accessible to DNA analysts, so results are easily explained in court.

NA

Input data

Training data was not used in the sense of AI software. OCME performed thousands of tests using the software to validate it for optimum use with our current laboratory standard operating procedures and genetic analyzers.

Forensic DNA profiles from crime scenes as well as the DNA profiles from victims and suspects of crimes.

The output is a deconvolution of genotype probability distribution that lists all of the accepted genotype sets and their associated weights. These weights can take any value from zero to one.

NicheVision Forensics, LLC

Professional services

The software has been developed by New Zealand Crown Institute of Environmental Science and Research with Forensic Science South Australia. The developer assisted OCME in analyzing and interpreting our data during the validation of the software.

NA

NA

NA

2025

Office of Technology and Innovation

Applications

311 Mobile App AI Smart Select

2025/12

Tool was created in CY2025

Information presentation

Classification

Informative

Continuous

Individuals

Users of the 311 mobile app

NA

Not specified

311 Smart Select allows users of the 311 mobile app to upload a photo of the problem they would like to report, and then provides suggestions of matching service request types. Users can select the results that best describes their issue, and then will be directed to a pre-filled mobile service request form or knowledge article.

The purpose of the tool is to provide users with the most relevant service request type for their problem. Users may encounter problems in the city but not know the precise language to register their complaint effectively. This tool ensures that 311 users are easily and accurately able to report issues, and that the city can triage and address concerns effectively.

NA

No identifying information is collected or disclosed

The training data is proprietary to Microsoft.

Images uploaded by the user. Images are virus scanned and stored to comply with Freedom of Information Law requirements.

Recommended service request types.

Microsoft, IBM

Off-the-shelf, Professional services

This feature is implemented using Microsoft Azure AI Foundry and Microsoft Azure AI Search. IBM is the professional services vendor who supported the development of the application.

NA

NA

NA

2025

Office of Technology and Innovation

Public Safety and Emergency Management

Fusus CORE Elite AI

2025/01

Tool was created in CY2025

Risk management

Classification

Monitored

Continuous

Property; Geographic space; Individuals

Individuals in areas where cameras are placed.

NA

https://www.axon.com/products/axon-fusus

Fusus CORE Elite AI enables real-time object detection, object classification, and alerting when connected to camera systems.

Fusus CORE Elite AI enhances operational situational awareness by detecting objects, people, or motion-based events in real time, such as a person or a vehicle entering a restricted area. The tool can be configured to identify specific detection parameters and create alert profiles for those parameters, allowing automated notifications to be sent to designated monitoring staff. Alerts are currently distributed to email groups monitored by OTI’s Technology Operations Center and Emergency Management personnel.

NA

Input data; Output data

Training data is proprietary to the vendor.

Live or recorded video streams from connected cameras, which are stored locally and subject to a limited retention period.

Detection alerts and labeled video metadata.

Fusus by Axon

Off-the-shelf, Professional services

This tool is part of the FUSUS video management system.

NA

NA

NA

2025

Office of Technology and Innovation

Web Operations

Google Translate

2013/00

No

Information presentation

Data transformation

Fully autonomous

Continuous

Individuals

Users of nyc.gov

NA

Not specified

Google Translate enables machine translation of nyc.gov and subpages into over 100 languages.

The Google Translate widget is used to make information on nyc.gov and its subpages more accessible to New Yorkers with limited English proficiency. It translates content into the 10 languages required under the language access law (Local Law 30 of 2017) and over 100 others.

NA

Input data; Output data

Training data is proprietary to Google.

Web content on nyc.gov and its subpages, written in English.

Translated web content into the selected language.

Google

Off-the-shelf

Google provides this service for free to organizations.

NA

NA

NA

2025

Office of Technology and Innovation

Office of Data Analytics

LLM Generation of Draft List of City Agencies and Governance Organizations

2025/09

Tool was created in CY2025

Data management

Data transformation

Informative

Once

Group, organization, or business

NA

NA

https://data.cityofnewyork.us/City-Government/NYC-Agencies-and-Governance-Organizations/t3jq-9nkf/about_data

This algorithmic tool was used to populate an initial draft of and manage updates to the NYC Agencies and Governance Organizations dataset: a standardized, analysis-ready, machine-readable reference of New York City agencies and other organizations with New York City-specific governance functions.

Large language models were employed in the creation of the NYC Agencies and Governance Organizations dataset to help institute acceptance rules for entity inclusion and naming conventions and implement a pipeline: ingest, normalize, de-dupe/record-link, validate, and publish, with Unicode/encoding fixes, name parsing, and auditable transformations at each step. Structured human quality assurance (peer review, change logs, and schema validation) preceded scripted exports to NYC Open Data, which then feeds the NYC.gov Agency Directory.

NA

Input data; Output data

Training data is proprietary to the vendor.

Internal and external sources on New York City agencies and governance organizations including Citywide Org Chart; Mayor’s Office of Operations’ list, as well as lists from the Department of Records and Information Services and the Department for Citywide Administrative Services.

A single, analysis-ready table with preferred names, common alternates, leadership, and reporting lines powers both public discovery (NYC.gov Agency Directory) and internal analytics.

Claude by Anthropic

Off-the-shelf

An off-the-shelf product was used to develop the tool.

NA

NA

NA

2025

Office of Technology and Innovation

Research & Collaboration

Microsoft Copilot Pilot Program

2025/06

Tool was created in CY2025

Information presentation

Data transformation

Informative

Daily, over the course of the testing period

Individuals

City staff who participated in the pilot

NA

https://www.microsoft.com/en-us/microsoft-365-copilot

From June to August of 2025, select staff at OTI tested Microsoft Copilot for Office 365 and Copilot Chat through a pilot program to determine whether the applications provided business users meaningful benefits, as well as how the applications would need to be managed and secured.

Microsoft Copilot for Office 365 and Copilot Chat were tested to better understand the products and their potential value for City staff. Testers were encouraged to use the applications as part of their routine work flows, completing specific tasks that ranged from legal analysis of proposed legislation, reviewing stakeholder feedback for policy documents, data analysis on survey results, to developing content for social media. While most test cases were experimental and did not have a public impact, some outputs may have been used in public communications.

NA

Training data; Input data; Output data

Training data is proprietary to Microsoft. For Copilot for Microsoft 365, the model was also tuned using information about the files and structure of the OTI tenant, known as the "knowledge graph."

Users could input text and attach files.

The tool output text, images, and documents, depending on the user prompt.

Microsoft

No-cost engagement, Off-the-shelf

Microsoft provided 50 trial licenses for testing Microsoft Copilot for Office 365 and made Copilot Chat available to 40 users (Copilot Chat is free for Microsoft users but turned off in the OTI-hosted tenant).

NA

NA

NA

2025

Office of Technology and Innovation

Applications

MyCity Chatbot

2023/09

Yes

Information presentation

Ranking

Fully autonomous

Continuous

Individuals

Primarily City residents, visitors, and business owners

NA

https://chat.nyc.gov

The NYC MyCity chatbot is a beta AI-powered chatbot that provides information and access to services for residents and businesses in New York City. The chatbot was decommissioned in February 2026.

The NYC MyCity chatbot is a beta AI-powered chatbot that provides information and access to services for residents and businesses in New York City. It’s currently focused on two main areas: Business Services and 311 information on City services. Basic information on MyCity is also included, but is a much smaller aspect of the index. The chatbot provides information on starting or operating a business in New York City, answers questions about permits, licenses, regulations, and other business requirements, and connects users with relevant resources and support services. It also offers information on various city services and benefits, especially those covered by 311 knowledge articles, and helps users find resources related to childcare, career, and other areas. The chatbot is using Microsoft’s Azure AI technology and OpenAI’s ChatGPT 4-o large language model.

The chatbot content was expanded to include 311 knowledge articles.

No identifying information is collected or disclosed

Training data is proprietary to the vendor.

Text queries are input by the user on the MyCity portal.

The tool produces text responses with references and links primarily based on information from Business Services and 311 knowledge articles.

Microsoft, EY

Procurement/Paid pilot, Professional services

Microsoft provides cloud-based ChatGPT services, and EY is the professional services vendor.

NA

NA

NA

2025

Office of Technology and Innovation

311

Omnichannel Language Translation

2024/01

No

Information presentation

Data transformation

Informative

Continuous

Individuals

Customers who contact 311 via text/SMS

NA

Not specified

The Omnichannel Language Translation tool delivers multi-language capability for the 311 text/SMS channel. The tool supports the 10 designated citywide languages to enable 311 agents to interact with customers in their language.

The algorithmic tool converts the customer’s text inquiry in their chosen language into English, allowing the text agent to understand, research and reply to the inquiry. The tool converts the agent’s English language response to the customer’s chosen language among the 10 designated citywide languages.

NA

No identifying information is collected or disclosed

The training data is proprietary to Microsoft.

Text/SMS inquiries from customers via 311-NYC.

Responses to customer inquiries in the language the customer used.

Microsoft

Procurement/Paid pilot

Omnichannel is part of the Microsoft suite available to OTI as part of the Dynamics customer relationship management platform. Microsoft supported the design, development, and testing of the tool preparation and deployment.

NA

NA

NA

2025

Office of Technology and Innovation

Office of Data Analytics

Zoom Automated Captions

2021/03

No

Information presentation

Data transformation

Fully autonomous

Captions were produced for 45 events from Open Data Week 2025

Individuals

Captions are provided to those who choose to watch the videos online.

NA

https://www.youtube.com/@NYCOpenDataWeek

Creates virtual closed captioning/live transcription during Zoom meetings.

Captions are provided to attendees of Zoom meetings held by the NYC Open Data team at the Office of Data Analytics in conjunction with the civic tech non-profit BetaNYC under the Open Data Week and Open Data Ambassador initiatives. The full transcription of the event is then added to the meeting recordings, which are uploaded on YouTube. The purpose of the captions, both for the live event and the recording, is to improve meeting accessibility.

NA

Input data; Output data

Training data is proprietary to Zoom.

Live audio from Zoom meeting.

Captions/transcript of meeting.

BetaNYC, Zoom

Off-the-shelf, Professional services

BetaNYC is our collaborator on the Open Data Week and Open Data Ambassador initiatives. They own and operate the Zoom account that is used for meetings under these initiatives, have access to the transcription files, and use these when editing and uploading video recordings onto YouTube.

NA

NA

NA

2025

Office of the Comptroller

Communications

Google Translate

2014/01

Yes

Information presentation

Data transformation

Fully autonomous

Daily

Individuals

NA

NA

https://translate.google.com/

Google Translate enables machine translation of Comptroller's website, its subpages, and printed materials to over 100 languages.

The Google Translate widget is used to make information on Comptroller's website, its subpages, and printed materials more accessible to New Yorkers with limited English proficiency. It translates content into 10 languages required under the language access law (Local Law 30 of 2017) and over 100 others.

Google Translate was updated by the vendor (Google), and our agency continued using the tool under the updated version.

Input data; Output data

Training data is proprietary to the vendor.

Web content on Comptroller's website and its subpages; content for printed materials written in English.

Translated content into the selected language.

Google

Off-the-shelf

Google Translate is a publicly available product.

NA

NA

NA

2025

Office of the Comptroller

Communications

Mirage

2025/01

Tool was created in CY2025

Information presentation

Data transformation

Monitored

Once a week

Individuals

Social media users who follow office socials

NA

https://mirage.app/

Mirage is a captioning tool used for social media videos. This tool assists staff to accurately add captions to the video visuals created by the Comptroller’s office for social media publishing.

This tool adds captions to the videos created by the Comptroller's office for social media.

NA

Input data; Output data

Training data is proprietary to the vendor.

Video taken for social media.

Video output with captions added to the input video.

Mirage

Off-the-shelf

Vendor created product is used for captioning.

NA

NA

NA

2024

Administration for Children's Services

Division of Policy, Planning, and Analysis

Accelerated Safety Analysis Protocol Tool

2018/05

No

Performance evaluation

Ranking

NA

NA

Individuals

NA

NA

NA

Predictions of Severe Harm (identifying likelihood of substantiated allegations of physical or sex abuse within the next 24 months) are based on a machine learning methodology and are calculated for all children involved in active investigations early in the investigation (day 10). An investigation is assigned a numeric likelihood of this outcome based on the child in the case with the highest likelihood. The ACS Quality Assurance unit in the Division of Child Protection reviews about 3,000 active investigations annually, selecting those with the highest likelihood of severe harm.

The Quality Assurance (QA) unit in the Division of Child Protection at ACS has the capacity to review about 3,000 investigation cases out of about 50,000 investigations annually. ACS developed this predictive model to support the selection of cases for QA review. Open investigations involving children with the greatest likelihood to experience future severe harm – substantiated allegations of physical or sex abuse in the following 24 months – are selected for review. The tool does not support decisions about services or interventions for individuals or families involved with ACS, beyond the selection of the case for this additional QA review.

If the QA review team identifies gaps in routine, required documentation or practice, the team speaks with the field office conducting the investigation and follows up to make certain these gaps have been addressed. Scores are not shared with staff in the QA unit or the investigative unit. The model only supports the decision about which investigations are prioritized for review by the QA unit.

NA

true

ACS trained the model on ACS historic administrative data about closed investigations from April 2014 to April 2016. The training set included about 142,026 observations. The model was tested on closed investigations from April 2016 to April 2017 with 53,477 observations.

Predictions are based on administrative data about prior and current child welfare involvement including investigations triggered by a New York State Central Register call and time spent in foster care. Only ACS administrative data are used in the model.

Rank ordered list of open investigation cases involving children with the highest likelihood to experience future severe harm, defined as substantiated allegations of physical or sex abuse in the following 24 months to be reviewed by a special QA Review Team.

None

NA

NA

NA

NA

NA

2024

Administration for Children's Services

Division of Policy, Planning, and Analysis

Caseloads Projection

2024/07

Created in CY2024

Resource allocation

Forecasting

NA

NA

Geographic space

NA

NA

NA

The model estimates weekly caseloads by area for the next 12 months from the run date (day 14 of the prevention case).

The model is a time series model that forecasts caseloads citywide. The forecast analysis was performed at a more granular geography for the borough level. The model accounts for seasonality, trend, and variation in data to accurately project future caseloads.

Staffing decisions are made based on projected caseloads, historical attrition rate, and current team size.

Caseloads projections help identify how busy certain neighborhoods are likely to be in the next 12 months to optimize workforce distribution across different boroughs in the city. As social workers complete their training program, they are assigned to field offices. The right number of Child Protective Service Social Workers needs to be assigned to each borough based on the workload.

NA

false

The model was trained on caseloads from January 2021 to July 2023 and tested on caseloads from July 2023 to July 2024.

Predictions are based on administrative data on investigations and Family Service Units’ involvement were extracted from CONNECTIONS (a child welfare computer system that provides for the documentation of information about families and children in New York State). A snapshot of all open investigations was taken at the start of the week (every Monday). This value was treated as the average caseload for the week. Only ACS administrative data are used in the model.

Forecast of number of open investigations involving children by borough.

None

NA

NA

NA

NA

NA

2024

Administration for Children's Services

Division of Policy, Planning, and Analysis

Housing Prioritization

2023/04

No

Resource allocation

Ranking

NA

NA

Individuals

NA

NA

NA

The model estimates a risk score for a child receiving prevention services whose family will apply and be eligible for a homeless shelter within 12 months from the start of service (day 14 of the prevention case).

The model helps predict the risk of application for homeless shelters among families receiving prevention services. With a limited number of vouchers available, the risk model helps ACS prioritize housing assistance for those families at greatest risk of becoming homeless.

The service provider meets with the family to conduct a qualitative assessment of the family’s housing needs and vouchers are offered based on their findings. This is one of multiple ways in which ACS can identify families potentially eligible for housing assistance.

The city has allocated 100 housing vouchers to families receiving ACS prevention services. The shelter application model identifies the likelihood of a family in prevention services applying for homeless shelter within 12 months beyond the current prevention case. The model uses a machine learning methodology and is calculated for all children in a prevention case. ACS prevention services uses the results as one of several ways of identifying possible families that service providers can assist in applying for shelter.

NA

false

ACS trained the model on ACS historic administrative data regarding preventive services started between 2014 and 2020. An 80/20 split of data to train on 80 percent and test on 20 percent ensuring that no family appears in both sets. The training set contains 140,242 observations between January 2014 and December 2020. The test set consisted of 34,508 observations between January 2014 and December 2020.

Predictions are based on administrative data about prior and current child welfare involvement at the start of a case. This includes State Central Registry investigations and time spent in foster care. Only ACS administrative data are used in the model.

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 a prevention service.

None

NA

NA

NA

NA

NA

2024

Administration for Children's Services

Division of Policy, Planning, and Analysis

Prevention Score Card

2021/09

No

Performance evaluation

Ranking

NA

NA

Individuals

NA

NA

NA

Predictions of Repeat Maltreatment (identifying the likelihood of being involved in a future indicated investigation within the next 24 months at the start of service) are based on a machine learning methodology and are calculated for all children receiving prevention services from ACS prevention service providers.

The Repeat Maltreatment model is used to make predictions on day 10 from the start of the prevention case to assess the risk of the family at the beginning of the service. A prevention case is assigned a numeric likelihood of an indicated investigation based on a New York State Central Register (SCR) within 24 months from the start of a prevention service.

The prevention providers are assessed for their performance based on the service needs/risk levels of the families they’ve served during the previous fiscal year.

The programs were sorted and ranked based on their average risk, and then divided into four quartiles by rank order: the top 25 percent of programs are classified as the Very High-Risk Cohort, the next 25 percent of programs as the High-Risk Cohort, the next 25 percent as the Medium-Risk Cohort, and the lowest 25 percent as the Low-Risk Cohort. Assignment to a cohort is not a way of performance assessment of the program but to group prevention service providers for fair comparisons based on the risk level of families they served.

NA

true

ACS trained the model on ACS historic administrative data about closed investigations from July 2009 to June 2016. Training set included about 158,787 observations. The model was tested on closed investigations from July 2016 to June 2018 with 46,969 observations.

Predictions are based on administrative data about prior and current child welfare involvement at the start of a case. This includes SCR investigations and time spent in foster care. Only ACS administrative data are used in the model.

The model is used for generating a scorecard of prevention service providers by categorizing prevention programs based on the average risk profile of the cases they served during the assessment year. These groupings of program cohorts provide context for understanding the scorecard, as it allows for performance comparison of programs that accepted and served families with similar risk profiles.

None

NA

NA

NA

NA

NA

2024

Commission on Racial Equity

NA

Adobe Express

2024/07

Created in CY2024

Information presentation

Data generation

NA

NA

Individuals

General public

NA

NA

Generates images in response to a verbal prompt.

Used to generate visual content to be adapted for agency use in social media.

NA

false

Training data is proprietary to Adobe Creative Cloud.

Description of image sought.

Image relevant to social media messaging.

None

NA

NA

NA

NA

NA

2024

Department of Environmental Protection

Bureau of Environmental Compliance

Idling Complaints Program

2022/08

No

Performance evaluation

Classification

NA

NA

Individuals

NA

NA

NA

A contractor helped create an AI tool that analyzes the audio and visual aspects of pictures and videos submitted by citizens of alleged car idling complaint occurrences that are in violation of New York City air pollution laws.

The analysis from the tool makes a recommendation to staff reviewers whether the submitted evidence support an occurrence of car idling in violation of New York City laws. The tool also provides a level of confidence in its recommendation. The tool does not make the review decision in the Idling Complaints system. It is still entirely up to the staff to decide whether to take the tool’s recommendation or not.

NA

false

Videos and pictures of cars idling submitted by citizens, along with staff decisions on whether the picture/video constituted as an idling violation.

Videos and pictures submitted by citizens through our web portal.

Recommendation, confidence level, description of its decision from the tool.

Acuvate

NA

Acuvate developed the AI tool that performs the automated analysis of the submitted evidence.

NA

NA

NA

2024

Department of Health and Mental Hygiene

Disease Control – Public Health Laboratory

Bowtie2

2022/06

No

Data management

Matching

NA

NA

Individuals; Biological sample

NA

NA

NA

Aligns sequencing data to a reference sequence. Bowtie2 aligns sequencing data to a reference using Burrows-Wheeler transformations. It is geared to use with Illumina sequencing data.

Bowtie2 is an intermediate step in the workflow to analyze COVID-19 variants in wastewater.

NA

false

N/A

Sequence reads (fastq) for single or paired-end runs (sequence reads can be considered strings).

Aligned reads in SAM format.

None

NA

NA

NA

NA

NA

2024

Department of Health and Mental Hygiene

Disease Control – Public Health Laboratory

Burrows-Wheeler Aligner (BWA)

2017/07

No

Data management

Matching

NA

NA

Individuals; Biological sample

NA

NA

NA

Aligns sequencing data to a reference sequence.

Burrows-Wheeler Aligner (BWA) is aligning sequence data to reference using Burrows-Wheeler transformations. This tool is optimal for low-divergent genomic data and short read data; such as Illumina sequence data. This tool is used to predict the order in which the fragments generated by sequencers are pieced together to form a complete genomic sequence data. This tool is used for Legionella and PulseNet sequencing analyses.

NA

false

N/A

Sequence reads (fastq) for single or paired-end runs (sequence reads can be considered strings).

Aligned reads in SAM format.

None

NA

NA

NA

NA

NA

2024

Department of Health and Mental Hygiene

Disease Control – Bureau of Investigations

ChoiceMaker (CM)

2003/06

No

Data management

Matching

NA

NA

Individuals

Individuals of all ages with a record in the Citywide Immunization Registry

NA

NA

ChoiceMaker (CM) is a record-matching tool that identifies duplicate records belonging to the same individual.

CM is used by the Bureau of Investigations (BOI) and Healthy Homes to identify duplicate immunization and lead records. The outputs produced by CM are used in ongoing manual and automated deduplication processes (record merging).

NA

true

The CM model was trained on human decisions.

CM uses demographic data (e.g., names, date of birth, address, identifiers) and health event data (e.g., date and type of event) from BOI’s Citywide Immunization Registry and Healthy Homes’ LeadQuest registry in its evaluation.

The program outputs a series of record pairs and a match probability for each pair.

HLN Consulting

NA

A vendor was involved in the development of the program initially. CM is now available as an open-source program. The DOHMH implementation is maintained by HLN Consulting.

NA

NA

NA

2024

Department of Health and Mental Hygiene

Disease Control - Public Health Laboratory

Genome Analysis Toolkit (GATK)

2017/10

No

Data management

Classification

NA

NA

Individuals; Biological sample

NA

NA

NA

A suite of tools for variant calling and filtering after sequence alignment. It uses naive Bayes to qualify aligned bases as sequence or erroneous data, which would be excluded from the final genomic sequence.

Used to identify mutations and call upon differences from the reference, which is used to generate the predicted complete sequence.

NA

false

Sets of known variant sites.

Fasta, uBam, SAM/BAM/CRAM, VCF.

BAM, TXT, VCF.

None

NA

NA

NA

NA

NA

2024

Department of Health and Mental Hygiene

Disease Control - Public Health Laboratory

Guppy

2020/06

No

Data management

Classification

NA

NA

Individuals; Biological sample

NA

NA

NA

Converts electric signals to predict a nucleotide and enables filtering of low-quality calls.

This is a tool designed specifically for Oxford Nanopore Technology (ONT) data. This is a neural network based basecaller: a tool that determines nucleotide bases of a genetic material and converts electric signals into strings to represent genomic data. In addition to basecalling, the tool also performs filtering of low-quality reads (a stretch of sequenced genetic material). This is the initial step that converts electric signals to fragments of sequence data, which can then be used for COVID-19 sequencing analysis.

NA

false

The default models within Guppy are trained on a mixture of native and amplified DNA/RNA, from multiple organisms including plant, animal, bacterial, and viral genomes.

DNA/RNA strand passing through the nanopore. Raw data is stored as .fast5 files.

.fast5, fastq, or BAM files.

Oxford Nanopore Technologies

NA

Developed and maintains the tool.

NA

NA

NA

2024

Department of Health and Mental Hygiene

Disease Control – Bureau of Immunization

Immunization Calculation Engine (ICE)

1997/00

No

Information presentation

Forecasting

NA

NA

Individuals

Anyone who needs a vaccine

NA

NA

The Immunization Calculation Engine (ICE) is an immunization evaluation and forecasting system whose default immunization schedule supports all routine childhood, adolescent, and adult immunizations based on the recommendations of the Advisory Committee on Immunization Practices. ICE is free and open-source available through https://cdsframework.atlassian.net/wiki/spaces/ICE/overview.

ICE is used by the Bureau of Immunization to evaluate a patient’s immunization history and generate appropriate immunization recommendations.

NA

true

N/A

ICE uses demographic data (e.g., date of birth) and vaccination data (e.g., immunization date, vaccine group and type) in the evaluation process. Data used are stored in the Citywide Immunization Registry.

The program returns recommendations on whether a patient has completed a vaccine series or is due for vaccines.

HLN Consulting

NA

A vendor was involved in the development of the program and continues to be involved in program enhancements. ICE is also available as an open-source program. The DOHMH implementation is maintained by HLN Consulting.

NA

NA

NA

2024

Department of Health and Mental Hygiene

Disease Control – Bureau of Communicable Disease

Improving Foodborne Disease Outbreak Detection by Incorporating Complaints Identified in Social Media Data

2016/11

No

Triage

Scoring

NA

NA

Individuals

Public who dine at NYC restaurants and are Yelp users and NYC restaurants

NA

NA

Restaurant-associated foodborne disease outbreaks are often identified through complaints received via NYC311 non-emergency information system; however not all individuals report to NYC311. DOHMH in collaboration with Columbia University developed a text classifier program which monitors Yelp and Twitter data to identify complaints of foodborne illness which was supported by grants from the Alfred P. Sloan Foundation and the National Science Foundation. As of April 2023, the tool no longer uses data from Twitter (X) due to application programming interface (API) changes.

The model uses data from Yelp restaurant reviews and previously used Twitter data that was available on Twitter’s publicly available API. Twitter (X) removed free access to their publicly available API in April 2023, so these data are no longer included in our analyses. The classifiers assign a “sick score” to each Yelp review indicating the likelihood that the review pertains to foodborne illness. The sick score is based on whether the review contains key words indicative of foodborne illness (e.g. “vomit”); the Yelp classifier also incorporates if the review indicates that multiple people became sick and if the review indicates a time between eating at a restaurant and illness onset (incubation period) that is consistent with foodborne illness. Each review with a sick score greater than or equal to a threshold value are reviewed and annotated by DOHMH foodborne disease epidemiology and environmental health staff to determine if the review was actually reporting foodborne illness possibly associated with a New York City restaurant; if yes, Yelp messages are sent to Yelp reviewers, requesting that they contact DOHMH. Data from annotations are used to improve classifier performance. Foodborne disease complaints identified through Yelp are combined with foodborne disease complaints reported to NYC311 to improve efficiency of outbreak detection.

NA

true

Training data was used in the development of the Yelp classifiers. The training data consisted of restaurant reviews obtained from Yelp by Columbia University; a subset of these data were joined with annotations provided by DOHMH staff. The annotations of restaurant reviews focused on the following: 1) if the review indicated foodborne illness; 2) if the incident occurred in the past 30 days; 3) if multiple people were sick; and 4) if the incubation period was consistent with foodborne illness. The training data is periodically updated (with annotations from DOHMH) to improve the classifiers.

Yelp reviews of New York City restaurants are pulled from a privately available API provided by Yelp.

The output data includes a “sick score” that the classifiers assign to each Yelp review indicating the likelihood that the review pertains to foodborne illness.

Columbia University

NA

DOHMH staff, including Bureau of Communicable Disease, Office of Environmental Investigations, and Division of Informatics and Information Technology & Telecommunications, and Columbia University are involved in making decisions about the tool. Columbia University Department of Computer Science professors and doctoral students maintain the classifier. The project was previously funded by the Alfred P. Sloan Grant, for which The Fund for Public Health in New York provided administrative support and grant management to DOHMH. This support and management ended at the completion of the grant in 2021.

NA

NA

NA

2024

Department of Health and Mental Hygiene

Disease Control - Public Health Laboratory

IQTREE

2020/05

No

Data management

Clustering

NA

NA

Individuals; Biological sample

NA

NA

NA

IQTREE uses maximum-likelihood regression to create phylogenetic trees from genomes.

Produced phylogenetic trees are used to help rule in or out outbreaks of COVID-19 or other organisms.

NA

false

N/A

FASTA, NEXUS, CLUSTALW, PHYLIP.

Readable report, maximum likelihood tree in NEWICK format, log file for entire run.

None

NA

NA

NA

NA

NA