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Algorithmic Tools Compliance Report

"This is an annual report on algorithmic tools used by City agencies, collected under Local Law 35 of 2022 (LL 35). It includes descriptions of the tool's use and purpose, datasets used, and vendor involvement. The report is also published as a PDF on the OTI website: https://www.nyc.gov/content/oti/pages/reports.

The full text of LL 35 is available online: https://legistar.council.nyc.gov/LegislationDetail.aspx?ID=4265421&GUID=FBA29B34-9266-4B52-B438-A772D81B1CB5

An "algorithmic tool" is defined by the law as: "Any technology or computerized process that is derived from machine learning, artificial intelligence, predictive analytics, or other similar methods of data analysis, that is used to make or assist in making decisions about and implementing policies that materially impact the rights, liberties, benefits, safety or interests of the public, including their access to available city services and resources for which they may be eligible. Such term includes, but is not limited to tools that analyze datasets to generate risk scores, make predictions about behavior, or develop classifications or categories that determine what resources are allocated to particular groups or individuals, but does not include tools used for basic computerized processes, such as calculators, spellcheck tools, autocorrect functions, spreadsheets, electronic communications, or any tool that relates only to internal management affairs such as ordering office supplies or processing payments, and does not materially affect the rights, liberties, benefits, safety or interests of the public."

City Government Office of Technology and Innovation (OTI) Dataset jaw4-yuem 27 fields
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Dataset fields
Showing 18 real records
Department of Investigation
Year: 2022 • Agency: Department of Investigation • Department: NA
Year
2022
Agency
Department of Investigation
Department
NA
Tool Name
Facial Recognition Technology
Date First Use
2019/03
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
The tool analyzes an uploaded image or video and searches and compares it with lawfully possessed images to generate a pool of possible matches. If possible matches are identified, a trained DOI examiner visually analyzes and evaluates potential matches to assess reliability of a match consistent with agency policy and applicable laws. A match serves as an investigative lead for additional investigative steps and does not constitute a positive identification.
Purpose Desc
Facial recognition is a digital technology that DOI uses to analyze uploaded images or videos of people and objects obtained during an investigation by comparison with lawfully possessed images. Facial recognition generates possible matches of an object or individual from this analysis and comparison. The purpose of the tool is to assist DOI investigations of matters within its jurisdiction including fraud and other criminal activity.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training data: Self-trained in system usage.

Input data: Images.
Vendor
Out-of-the-box product. The vendor provides ongoing technical assistance. Confidentiality agreements are in place with the vendor.
Analysis Type
NA
Department of Social Services
Year: 2022 • Agency: Department of Social Services • Department: NA
Year
2022
Agency
Department of Social Services
Department
NA
Tool Name
Homebase Risk Assessment Questionnaire (RAQ)
Date First Use
2012/06
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
Homebase applicants answer questions about their current housing situation, history of disruptive experiences, and shelter history. Each of the answers is assigned a number of points, and applicants that reach a certain point threshold are eligible for additional Homebase services such as financial assistance and case management. Workers are able to override a limited number of model decisions with permission of a supervisor.
Purpose Desc
The Homebase program was created to prevent households from entering the DHS shelter system. Since NYC has a range of antipoverty programs and the number of households entering shelter is small compared to the pool of New Yorkers who enrolled in public assistance or have an eviction filing each year, the Agency had to ensure that the households who most needed additional homelessness prevention services were being enrolled in Homebase programs. Research showed that staff were not accurately able to predict who would or would not enter the DHS shelter system and that using a risk assessment would provide a much better way to match resources to the families who would benefit the most.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training data: The RAQ was developed based on analysis of data on Homebase enrollees from 2004 to 2008, conducted in conjunction with a team of academic researchers, to determine predictive factors for those entering shelter.

Input data: Personal characteristics such as age and pregnancy; educational attainment, employment status, benefits status; housing issues such as eviction, discord, number of moves, recent discharge from institutions; traumatic childhood experiences; past and recent experience of homelessness.
Vendor
DHS contracted with researchers to evaluate years of Homebase administrative data to develop a risk assessment. The published research papers are listed below: https://ajph.aphapublications.org/doi/10.2105/AJPH.2013.301468 https://www.journals.uchicago.edu/doi/abs/10.1086/686466?mobileUi=0&journalCode=ssr
Analysis Type
NA
Fire Department
Year: 2022 • Agency: Fire Department • Department: NA
Year
2022
Agency
Fire Department
Department
NA
Tool Name
EMD Schedule Optimization Tool
Date First Use
2021/06
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
The algorithm requires two datasets. First, the tool requires the average number of medical calls per hour for a 24-hour period. Second, the tool requires a user to specify the number of call takers assigned to each tour. Based on these two inputs, the tool provides a projection of supply (call takers) versus demand (medical calls). Additionally, the tool can take the total number of available staff and optimally allocate them across tours to maximize the minimum difference between supply and demand. Based on these outputs, EMD officers can identify times during the day when call taker utilization is high and reallocate staff to accommodate.
Purpose Desc
The purpose of the tool is to provide Emergency Medical Dispatchers (EMD) staff a tool to optimally allocate call takers during a 24-hour period. The tool uses an expected number of incoming calls and the number of personnel scheduled to work in order to allocate the call takers to different shifts such that the supply of call takers exceeds the demand for call takers.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training data: This is an optimization model and was not "trained" using training data. The algorithm relies on actual historical data to determine average hourly medical calls.

Input data: The tool requires an hourly count of medical calls arriving during a 24-hour period. Additional "data" requirements are input from the user depending on user-driven scenarios. For example, a user could specify five 8-hour tours per day (at different start times) rather than existing four tours (2 8-hour tours and 2 12-hour tours).
Vendor
The tool was developed internally at FDNY in partnership with Columbia University's Industrial Engineering and Operations Research Department.
Analysis Type
NA
Fire Department
Year: 2022 • Agency: Fire Department • Department: NA
Year
2022
Agency
Fire Department
Department
NA
Tool Name
EMS Ambulance Scheduling Tool
Date First Use
2021/06
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
The tool requires the average number of medical emergencies per hour by dispatch area and the ambulance schedule of each dispatch area. Based on this information, the algorithm will optimize tour start times to maximize the minimum difference between the supply of ambulances and the demand for an ambulance.
Purpose Desc
The purpose of the tool is to match the supply of EMS ambulances to the demand for ambulances (medical emergencies) over a 24-hour period for each EMS dispatch area. The tool uses an existing ambulance schedule for each dispatch area and optimizes their start times in order to match the demand for an ambulance. The tool supports FDNY EMS in developing an ambulance schedule citywide.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training data: This is an optimization model and was not "trained". The model optimizes based on the hourly average number of incidents in a dispatch area.

Input data: There are two input data sources. First, the supply of ambulances which includes the start and end times of each ambulance tour in the city, as well as the starting location of the ambulance. The second input is the demand, which is the number of medical emergencies per hour per dispatch area.
Vendor
The tool was developed internally at FDNY in partnership with Columbia University's Industrial Engineering and Operations Research Department.
Analysis Type
NA
Fire Department
Year: 2022 • Agency: Fire Department • Department: NA
Year
2022
Agency
Fire Department
Department
NA
Tool Name
EMS Hospital Load Balancing Algorithm
Date First Use
2021/01
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
The algorithm requires three data inputs: the estimated travel time from any ATOM to any hospital, the number of available beds for every hospital and the estimated number of transports that will occur the following day at every hospital. The algorithm first determines if any hospital is expected to receive more patients than available beds. If overload is expected, the algorithm reallocates the necessary ATOMs such that no hospital is overloaded, and the reallocation of any ATOM is done so with minimal additional travel time. The optimized output - known as a pattern - is directly input into the EMS CAD system for use in the following day.
Purpose Desc
The hospital load balancing algorithm is designed to optimize hospital transports in a way that proactively avoids hospitals from being congested with too many patients, while at the same time minimize the total travel times as much as possible. The outputs of the algorithm are used in the EMS Computer Aided Dispatch (EMS CAD) system to provide EMS crews with an optimal hospital to transport patients.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training data: This is an optimization model and was not "trained".

Input data: There are three input data sources. First is an ATOM-to-hospital travel time matrix. This matrix is computed by FDNY based on historical travel time information. Second, the algorithm consumes hospital bed availability data from the HERDS dataset generated by the New York State Department of Health and provided by NYC DOHMH. Finally, we estimate the number of transports that will occur at each hospital for the next day by using historical data and computing a 3-day moving average.
Vendor
The tool was developed internally at FDNY in partnership with Columbia University's Industrial Engineering and Operations Research Department.
Analysis Type
NA
Fire Department
Year: 2022 • Agency: Fire Department • Department: NA
Year
2022
Agency
Fire Department
Department
NA
Tool Name
EMS Hospital Suggestion Algorithm
Date First Use
2007/03
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
The algorithm computes a list of hospitals in order of closest to furthest in time for each medical condition category as currently established. (For example, there is a list of hospitals computed in order of closest in time for all hospitals that accept General Emergency Department patients and for all hospitals that accept special conditions, such as burns). Depending on the medical needs category of the patient, the algorithm produces a pre-determined list of hospitals which is based on the location of the patient and then made available to the crew as a list of "closest, most appropriate hospitals."
Purpose Desc
The EMS Hospital Suggestion Algorithm is used to determine the closest, appropriate hospital to the incident location based on the needs of a patient requiring transport.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Input data: The EMS Hospital Suggestion algorithm relies on telematics data from the Department of Citywide Administrative Services city-owned vehicles collected between 2015 and mid-2016 to calibrate a network analysis model that derives incident to hospital transport times. The order of suggested hospitals are then compared with five years of historical EMS hospital transport data from before the COVID-19 pandemic (2015-2019) to validate and correct the network model.
Vendor
This algorithm and the resulting output file that is used in our EMS CAD system to suggest hospitals was provided by a vendor, until September 2020. The Department currently creates this file using a new algorithm, developed in-house by the Bureau of Management Analysis and Planning in conjunction with engineers from Columbia University.
Analysis Type
NA
Fire Department
Year: 2022 • Agency: Fire Department • Department: NA
Year
2022
Agency
Fire Department
Department
NA
Tool Name
EMS Unit Suggestion Algorithm
Date First Use
2007/03
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
The algorithm computes a list of geographic atoms in order of closest to furthest in time for each atom in the city. This list of ordered atoms is the output of an algorithm that relies on a calibrated network model to derive travel time estimates. The output is an excel file which is converted into an EMSCAD-compatible file and loaded into the system for real-time unit selection capabilities. The file is generated and implemented as a 24/7 source file, meaning, the recommended search order is not currently varying by time of day. The Department is intending to implement time-of day search orders in the near future.
Purpose Desc
The EMS Unit Suggestion Algorithm is used to determine which order of geographic regions (known as atoms) to search in order for the EMSCAD system to select an appropriate EMS unit for dispatch to an incident.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Input data: The EMS Unit Suggestion algorithm relies on historical FDNY CAD trip time data which is used to calibrate a network analysis model which derives atom-to-atom transport times.
Vendor
This algorithm and the resulting output file that is used in our EMS CAD system to suggest atom order for unit search is currently provided by a vendor, Deccan International.
Analysis Type
NA
Fire Department
Year: 2022 • Agency: Fire Department • Department: NA
Year
2022
Agency
Fire Department
Department
NA
Tool Name
RBIS (Risk Based Inspection Program); ALARM (A Learning Approach to Risk Modeling)
Date First Use
2019/11
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
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 1-9), with 1 being highest risk and 9 being lowest. Buildings are prioritized within each of the nine risk scores according to the residential population in each building.
Purpose Desc
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 BINs), as a part of the Risk-Based Inspection Program.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training data: In order to create the models, the team utilized a 5-year incident dataset and reserved 99% of the data to train the probability model and 80% of the data to train the impact model.

Input data: The ALARM risk score utilizes data from our fire and EMS dispatch system, building characteristic data, 311 calls, felony crimes, census data and civilian injury data.
Vendor
ALARM was built in-house by a team of analysts from the Management, Analysis and Planning Bureau.
Analysis Type
NA
Mayor's Office
Year: 2022 • Agency: Mayor's Office • Department: NA
Year
2022
Agency
Mayor's Office
Department
NA
Tool Name
Methodology for Poll Site Language Assistance - MO - CEC
Date First Use
2020/11
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
Since no dataset is currently available that reliably captures the number of limited English proficient (LEP) registered voters for all program languages, the 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 NYCBOE 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.
Purpose Desc
This is a methodology for determining how the New York City Civic Engagement Commission (CEC) will provide interpretation services at poll sites for limited English proficient voters. The methodology explains how the NYCCEC will identify the languages and locations in which interpretation services will be offered during the November 2020 election and beyond. These services supplement the interpretation assistance provided by NYC Board of Elections in several languages. Under the Charter, the NYCEC can only provide interpretation services in a language if: (1) it is a designated citywide language; or (2) 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.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Input data: For citywide estimates, this methodology uses current data from the American Community Survey (ACS) 2016-2020 5-year estimates. This methodology also uses the American Community Survey Census Tract 2016-2020 5-year Public Use Microdata Samples for poll site level analysis; this is the most current and accurate data available on 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.
Vendor
None
Analysis Type
NA
Mayor's Office
Year: 2022 • Agency: Mayor's Office • Department: NA
Year
2022
Agency
Mayor's Office
Department
NA
Tool Name
Scorecard Blockface Sampling Algorithm - MO - Operations
Date First Use
2022/03
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
The Scorecard program sends inspectors across New York City to rate street and sidewalk cleanliness. The sampling algorithm creates a monthly list of blocks for inspectors to visit and rate.
Purpose Desc
The primary goal of the algorithm is to produce a sample of blockfaces that is statistically sound and geographically representative. This list is used to rate street and sidewalk cleanliness citywide, as well as by borough and DSNY district.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
The blockface sample is selected from the Pavement Edge File, which is part of the NYC Planimetric Database managed by the Office of Technology and Innovation. Sampling is weighted towards blockfaces in high-density areas and includes extra sampling of blockfaces in Business Improvement Districts (BIDs). It also takes into account the linear miles of street within a DSNY District.
Vendor
The sampling algorithm was developed by the former Mayor's Office of the Chief Technology Officer in partnership with the Mayor's Office of Operations.
Analysis Type
NA
Mayor's Office
Year: 2022 • Agency: Mayor's Office • Department: NA
Year
2022
Agency
Mayor's Office
Department
NA
Tool Name
SmartVAN / TargetSmart - MO - PEU
Date First Use
2019/11
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
The Mayor's Public Engagement Unit (PEU) uses SmartVAN to manage outreach across a range of projects. SmartVAN provides functionality to create lists of potential clients to contact, collect personal information and survey responses from clients, and conduct outreach via phone banks and canvassing. SmartVAN also contains a frequently updated commercial dataset, provided by TargetSmart, of New York City residents and their demographic, contact, and other information. PEU uses this preloaded data to create outreach lists when data on existing clients or from partner agencies is unavailable or insufficient to meet the scope of the outreach project.
Purpose Desc
In 2022, PEU has the TargetSmart data within SmartVAN on a number of projects. PEU frequently uses the data to create lists of residents who live within certain zip codes that PEU wants to target for outreach. For example, PEU created lists in SmartVAN used to conduct text and phone outreach to help New Yorkers access the Affordable Connectivity Program (ACP) in specific zip codes. In cases like these, TargetSmart's determination of who lives in which zip codes affects whether New Yorkers receive PEU outreach. Additionally, the algorithm that TargetSmart uses to match phone numbers to individuals and determine if they are mobile phones or not determines the type of outreach that New Yorkers receive.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
The algorithmically-derived data that PEU accesses via SmartVAN is the output of proprietary algorithmic processes developed and operated by TargetSmart. These algorithmic processes include matching multiple input datasets to determine residency, contact information, and demographics on New York City residents. SmartVAN also includes a number of algorithmically-determined likelihood scores, including scores for the likelihood that a household contains children under 18, etc.
Vendor
EveryAction and TargetSmart jointly provide the SmartVAN product. EveryAction is the software provider. TargetSmart is the data provider. TargetSmart is the entity who applies algorithmic techniques. EveryAction provides access to this data through their platform.
Analysis Type
NA
New York Police Department
Year: 2022 • Agency: New York Police Department • Department: NA
Year
2022
Agency
New York Police Department
Department
NA
Tool Name
Facial Recognition Technology
Date First Use
2011/10
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
Tool which may help investigators identify unknown subjects in law enforcement investigations.
Purpose Desc
Facial recognition is a digital technology that NYPD uses to compare images obtained during investigations with lawfully possessed arrest 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.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
•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.
• Training data is proprietary to the vendor.
Vendor
Software developed and maintained by Dataworks
Analysis Type
NA
New York Police Department
Year: 2022 • Agency: New York Police Department • Department: NA
Year
2022
Agency
New York Police Department
Department
NA
Tool Name
Patternizr
Date First Use
2016/10
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
Aids crime analysis in detection of potential crime patterns.
Purpose Desc
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.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
• 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.
Vendor
The tool was developed by data scientists and analysts at NYPD. Contractors and NYPD personnel integrated it into the Domain Awareness System. Personnel in Crime Control Strategies work with the Information Technology Bureau to maintain the tool.
Analysis Type
NA
New York Police Department
Year: 2022 • Agency: New York Police Department • Department: NA
Year
2022
Agency
New York Police Department
Department
NA
Tool Name
ShotSpotter
Date First Use
2015/03
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
Provides acoustic gunshot detection to assist with emergency call response
Purpose Desc
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.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
•Specialized software analyzes audio signals for potential gunshots, 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.
• Training data is proprietary to the vendor.
Vendor
Software developed and maintained by ShotSpotter
Analysis Type
NA
NYC Public Schools
Year: 2022 • Agency: NYC Public Schools • Department: NA
Year
2022
Agency
NYC Public Schools
Department
NA
Tool Name
MySchools
Date First Use
2018/08
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
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. Perhaps most common is its use 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
Purpose Desc
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 (3K, pre-K, Gifted & Talented, middle school, and high school). The tool encompasses a family-facing portal, a school-facing portal, and an administrative portal.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training data: The algorithm was already widely recognized for its advantages prior to adoption in New York City. The DOE consulted with a team of researchers at MIT who had been closely involved in its initial creation when we adopted it.

Input data: 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).
Vendor
We have a 5 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 the Department of Education, by the end of the contract. The team at DIIT has already begun to takeover maintenance and development of the tool.
Analysis Type
NA
NYC Public Schools
Year: 2022 • Agency: NYC Public Schools • Department: NA
Year
2022
Agency
NYC Public Schools
Department
NA
Tool Name
NYCDOE APPR Measures of Student Learning (MOSL) Growth Model
Date First Use
2013/09
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
The growth model uses a variety of student-level (assessment scores, English Language Learner, Disability, and Economic Disadvantage indicators), classroom-level (e.g. % Students With Disabilities), and school-level data (e.g. % English Language Learners, % 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 used to either 1) identify "peer groups" of students, from which student growth percentiles (SGPs) are determined, or 2) 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 MOSL Rating. The MOSL Rating is combined with the MOTP 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.
Purpose Desc
In accordance with New York state law and New York State Education Department (NYSED) regulations, the Department developed and maintains a "growth model" to produce Measures of Student Learning (MOSL) ratings for use in annual professional performance reviews (APPR) for teachers and principals. The MOSL ratings are combined with Measures of Teaching/Leadership Practice (MOTP/MOLP) ratings to produce an annual Overall Rating for each eligible educator.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training 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.

Input data: The growth model makes use of three types of data: (1) students’ end-of-year assessment scores, (2) enrollment and attendance records that link students to teachers and schools, and (3) historical academic and demographic information used to identify groups of similar students.
Vendor
Education Analytics provides technical assistance and quality assurance for the growth model.
Analysis Type
NA
NYC Public Schools
Year: 2022 • Agency: NYC Public Schools • Department: NA
Year
2022
Agency
NYC Public Schools
Department
NA
Tool Name
NYCDOE APPR Measures of Teaching/Leadership Practice (MOTP/MOLP) Calculation
Date First Use
2013/09
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
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 an MOTP/MOLP Rating. The MOTP/MOLP Rating is combined with the MOSL Rating to produce an Overall Rating for each eligible educator. 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.
Purpose Desc
In accordance with New York state law and New York State Education Department (NYSED) regulations, the Department developed and maintains databases and calculation rules to produce Measures of Teaching/Leadership Practice (MOTP/MOLP) ratings for use in annual professional performance reviews (APPR) for teachers and principals. The MOTP/MOLP ratings are combined with Measures ofStudent Learning (MOSL) ratings to produce an annual Overall Rating for each eligible educator.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training data: 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.

Input data: Rubric component numerical ratings.
Vendor
None
Analysis Type
NA
Office of Chief Medical Examiner
Year: 2022 • Agency: Office of Chief Medical Examiner • Department: NA
Year
2022
Agency
Office of Chief Medical Examiner
Department
NA
Tool Name
STRMix
Date First Use
2017/01
Updated
NA
Purpose Type
NA
Computation Type
NA
Autonomy
NA
Frequency
NA
Population Type
NA
Population Type Individual
NA
Population Type Other
NA
Website
NA
Tool Desc
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.  It grades them against the evidential sample, finding the combinations that best explain the profile.  A range of Likelihood Ratio options are provided for subsequent comparisons to reference profiles. Using a Markov Chain Monte Carlo engine, 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.
Purpose Desc
STRMix is a probabilistic genotyping tool that is used to analyze mixtures of DNA profiles to help associate the crime scene evidence to potential victims or suspects of crimes.
Updated Desc
NA
Identifying Info
NA
Data Training
NA
Data Input
NA
Data Output
NA
Vendor Name
NA
Vendor Type
NA
Vendor Desc
NA
Data 2022
Training data: Training data was not used in the sense of AI software. The OCME performed thousands of tests using the software to validate it for optimum use with our current laboratory standard operating procedures and genetic analyzers.

Input data: Forensic DNA profiles from crime scenes as well as the DNA profiles from victims and suspects of crimes.
Vendor
The software has been developed by New Zealand Crown Institute of Environmental Science and Research (ESR) with Forensic Science South Australia. The developer assisted the NYC OCME analyze and interpret our data during the validation of the software.
Analysis Type
NA
NYC Data AI • Public-data exploration interface