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インシデント 9: NY City School Teacher Evaluation Algorithm Contested

概要: An algorithm used to rate the effectiveness of school teachers in New York has resulted in thousands of disputes of its results.

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組織

すべての組織を表示
推定: New York city Dept. of Educationが開発し提供したAIシステムで、Teachersに影響を与えた

インシデントのステータス

インシデントID
9
レポート数
7
インシデント発生日
2012-02-25
エディタ
Sean McGregor
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

CSETv0 分類法のクラス

分類法の詳細

Problem Nature

Indicates which, if any, of the following types of AI failure describe the incident: "Specification," i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; "Robustness," i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; "Assurance," i.e. the system could not be adequately monitored or controlled during operation.
 

Unknown/unclear

Physical System

Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.
 

Software only

Level of Autonomy

The degree to which the AI system(s) functions independently from human intervention. "High" means there is no human involved in the system action execution; "Medium" means the system generates a decision and a human oversees the resulting action; "low" means the system generates decision-support output and a human makes a decision and executes an action.
 

Medium

Nature of End User

"Expert" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; "Amateur" if the AI systems were primarily meant to benefit the general public or untrained users.
 

Amateur

Public Sector Deployment

"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
 

Yes

Data Inputs

A brief description of the data that the AI system(s) used or were trained on.
 

School grades, student grades, predicted grades

CSETv1 分類法のクラス

分類法の詳細

Incident Number

The number of the incident in the AI Incident Database.
 

9

AI Tangible Harm Level Notes

Notes about the AI tangible harm level assessment
 

3.5 - the value-added measurement/modeling is not AI - it is a statistical model

Special Interest Intangible Harm

An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.
 

no

Date of Incident Year

The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank. Enter in the format of YYYY
 

2012

Estimated Date

“Yes” if the data was estimated. “No” otherwise.
 

Yes

Multiple AI Interaction

“Yes” if two or more independently operating AI systems were involved. “No” otherwise.
 

no

MIT 分類法のクラス

Machine-Classified
分類法の詳細

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.3. Lack of capability or robustness

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. AI system safety, failures, and limitations

Entity

Which, if any, entity is presented as the main cause of the risk
 

Human

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Intentional

インシデントレポート

レポートタイムライン

A NYC Math Teacher Fights Back After Receiving an Unfair 'Unsatisfactory' Rating from a Principal+3
New York City Teacher Ratings: Teacher Data Reports Publicly Released Amid Controversy
Teachers plan widespread appeals of 'unfair' evaluationsMaster teacher suing New York state over ‘ineffective’ rating is going to courtNew data show more than half of NYC teachers judged, in part, by test scores they don’t directly affect
A NYC Math Teacher Fights Back After Receiving an Unfair 'Unsatisfactory' Rating from a Principal

A NYC Math Teacher Fights Back After Receiving an Unfair 'Unsatisfactory' Rating from a Principal

parentadvocates.org

New York City Teacher Ratings: Teacher Data Reports Publicly Released Amid Controversy

New York City Teacher Ratings: Teacher Data Reports Publicly Released Amid Controversy

huffingtonpost.com

Reign Of Error: The Publication Of Teacher Data Reports In New York City

Reign Of Error: The Publication Of Teacher Data Reports In New York City

shankerinstitute.org

Analyzing Released NYC Value-Added Data Part 2

Analyzing Released NYC Value-Added Data Part 2

garyrubinstein.teachforus.org

Teachers plan widespread appeals of 'unfair' evaluations

Teachers plan widespread appeals of 'unfair' evaluations

politico.com

Master teacher suing New York state over ‘ineffective’ rating is going to court

Master teacher suing New York state over ‘ineffective’ rating is going to court

washingtonpost.com

New data show more than half of NYC teachers judged, in part, by test scores they don’t directly affect

New data show more than half of NYC teachers judged, in part, by test scores they don’t directly affect

cityandstateny.com

A NYC Math Teacher Fights Back After Receiving an Unfair 'Unsatisfactory' Rating from a Principal
parentadvocates.org · 2004

Stories & Grievances

A NYC Math Teacher Fights Back After Receiving an Unfair 'Unsatisfactory' Rating from a Principal

Edmond Farrell uses the Freedom of Information Law and Department of Education/Teacher regulations in his fight to change…

New York City Teacher Ratings: Teacher Data Reports Publicly Released Amid Controversy
huffingtonpost.com · 2012

The New York City Department of Education released today a list of individual ratings of thousands of the city's schoolteachers, a move that concludes a lengthy legal battle waged by the local teachers' union and media.

The Teacher Data Rep…

Reign Of Error: The Publication Of Teacher Data Reports In New York City
shankerinstitute.org · 2012

Late last week and over the weekend, New York City newspapers, including the New York Times and Wall Street Journal, published the value-added scores (teacher data reports) for thousands of the city’s teachers. Prior to this release, I and …

Analyzing Released NYC Value-Added Data Part 2
garyrubinstein.teachforus.org · 2012

In part 1 I demonstrated there was little correlation between how a teacher was rated in 2009 to how that same teacher was rated in 2010. So what can be more crazy than a teacher being rated highly effective one year and then highly ineffec…

Teachers plan widespread appeals of 'unfair' evaluations
politico.com · 2013

Teachers plan widespread appeals of 'unfair' evaluations

ALBANY—Hundreds of teachers in urban school districts plan to appeal performance evaluations that could be used as grounds for termination under a new statewide system for evaluating …

Master teacher suing New York state over ‘ineffective’ rating is going to court
washingtonpost.com · 2015

Former New York state education commissioner John King, who is being sued by a N.Y. teacher over the state’s educator evaluation system. (Mike Groll/AP)

A veteran teacher suing New York state education officials over the controversial metho…

New data show more than half of NYC teachers judged, in part, by test scores they don’t directly affect
cityandstateny.com · 2017

Just over half of New York City teachers were evaluated in the 2015–16 school year, in part, by tests in subjects or of students they didn’t teach, according to data obtained by Chalkbeat through a public records request.

At 53 percent of c…

バリアント

「バリアント」は既存のAIインシデントと同じ原因要素を共有し、同様な被害を引き起こし、同じ知的システムを含んだインシデントです。バリアントは完全に独立したインシデントとしてインデックスするのではなく、データベースに最初に投稿された同様なインシデントの元にインシデントのバリエーションとして一覧します。インシデントデータベースの他の投稿タイプとは違い、バリアントではインシデントデータベース以外の根拠のレポートは要求されません。詳細についてはこの研究論文を参照してください

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