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AI-Assisted Teacher and Principal Performance Scoring

What it is for

Employment & Work
  • The system supports annual professional performance reviews for teachers and principals. Per New York State Education Law 3012-d, the resulting Overall Rating is a significant factor in HR decisions including employment, tenure, salary, and other professional opportunities.

NYC Automated Decision Systems Use Case Register — MOTP/MOLP Calculation

Run by
NYC Public Schools (NYCPS)
Where
No fixed location
Kept
Retained Not specified in the register entry
Shared with
Accountable organization, Me

What it is for

Employment & Work

This system calculates annual performance ratings for New York City public school teachers and principals by weight-averaging rubric scores collected during classroom observations. The resulting score is combined with student learning measures to produce an Overall Rating that state law requires be used as a significant factor in employment, tenure, and salary decisions. All inputs are numerical rubric scores; no personally identifying information is reported as collected or disclosed by the system itself.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Rubric component numerical ratings collected by evaluators during multiple classroom or school observations throughout the school year. These are structured numerical scores on one or more rubric components per observation, not personally identifying data.

NYC Automated Decision Systems Use Case Register — MOTP/MOLP Calculation

Processing

Classification & Prediction
  • Rubric component scores are weight-averaged according to collectively bargained rules to produce a single MOTP/MOLP Rating. Pilot data prior to program launch informed the weights assigned to rubric components, though weights are ultimately determined through collective bargaining.

NYC Automated Decision Systems Use Case Register — MOTP/MOLP Calculation

What it does

Deciding (Analytical AI)
Human decides
  • The system scores and ranks educators by weight-averaging rubric component scores according to collectively bargained rules. Human evaluators supply the underlying rubric scores; the system computes the weighted average. The resulting MOTP/MOLP rating is then reviewed and used by HR decision-makers.

NYC Automated Decision Systems Use Case Register — MOTP/MOLP Calculation

Outputs

Sensitive personal information
Pseudonymous data
  • A MOTP/MOLP score for each eligible teacher or principal. This score is combined with a Measures of Student Learning (MOSL) rating to produce an Overall Rating for each educator. The register states no identifying information is collected or disclosed, but the score is tied to specific individuals within the school system's HR processes.

NYC Automated Decision Systems Use Case Register — MOTP/MOLP Calculation

Run by

NYC Public Schools (NYCPS)
  • NYC Public Schools, through its Division of Instructional and Information Technology, developed and maintains the databases and calculation rules that produce MOTP/MOLP ratings for use in annual professional performance reviews.

NYC Automated Decision Systems Use Case Register — MOTP/MOLP Calculation

Built by

Not stated by the NYC Office of Technology and Innovation.

Kept for

Retained Not specified in the register entry
  • The register does not specify a retention period. Given that ratings are used for tenure and HR decisions, records are likely retained for the duration of an educator's employment and potentially beyond, but no explicit policy was provided.
  • Duration: Not specified in the register entry

Shared with

Available to the accountable organization
  • MOTP/MOLP ratings are available to NYC Public Schools and used internally in HR decision-making processes. Evaluators, HR staff, and relevant administrators access the scores through internal systems.
Available to me
  • Educators who are rated receive their own MOTP/MOLP and Overall Ratings as part of the annual professional performance review process, consistent with collectively bargained agreements.

Stored

Stored locally
  • NYC Public Schools maintains databases for this system. Storage location is within NYC jurisdiction consistent with city and state education data governance, though specific storage infrastructure is not disclosed in the register.
  • Duration: Not specified in the register entry
How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the NYC Office of Technology and Innovation, not the vendor.

Your rights

  • Right to Algorithmic TransparencyEducators subject to this system have a right to understand how their MOTP/MOLP rating is calculated. The publicly available AWA Guide (https://tools.nycenet.edu/resources/awa-guide/) documents the calculation methodology. The weighting rules are collectively bargained and therefore available through union agreements.
  • Right to a Human ReviewThe register describes the system's autonomy as 'Supervised,' indicating human oversight is present. HR decisions informed by MOTP/MOLP scores are made by human administrators, not directly by the algorithm. Educators may have rights under collective bargaining agreements to contest HR decisions that rely on these ratings. The specific mechanism is not detailed in the register entry.

Risks and safeguards

  • Civil liberties harmThe MOTP/MOLP rating is a significant factor in employment, tenure, and salary decisions per state law, creating due-process and fairness concerns if rubric scores reflect evaluator bias, inconsistency, or disparate impact on protected groups. Risk: Educators may lack meaningful ability to contest algorithmically derived composite scores that affect their livelihoods.Safeguard: Weights are determined through collective bargaining rather than unilateral administrative discretion, providing a negotiated check. Evaluators conduct multiple observations throughout the year. The system has been in use since 2013 with oversight by the state education department. However, the register does not describe explicit bias audits, disparity analyses, or formal appeals mechanisms for the calculated scores.
  • Loss of autonomyBecause ratings are mandated by state law to be a significant factor in HR decisions, individual educators have limited ability to opt out of or challenge the composite scoring system. The algorithm translates human rubric scores into a composite that carries significant weight in consequential career decisions, potentially constraining professional agency.Safeguard: The collectively bargained nature of the weighting rules provides a degree of democratic accountability. The register notes the tool's autonomy level as 'Supervised,' indicating human evaluators and HR decision-makers remain in the loop. However, no mechanism for individual contestation of the MOTP/MOLP score itself is described.