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Incident #162·AI Safety & Failures

ETS Used Allegedly Flawed Voice Recognition Evidence to Accuse and Assess Scale of Cheating, Causing Thousands to be Deported from the UK

January 1, 20141 reportsView on AIID

Explainability

Description

International testing organization ETS admits voice recognition as evidence of cheating for thousands of previous TOEIC test-takers that reportedly included wrongfully accused people, causing them to be deported without an appeal process or seeing their incriminating evidence.

Actors

Alleged Deployers

ets

Alleged Developers

ets

Harmed Parties

uk-home-office
uk-ets-test-takers
uk-ets-past-test-takers

Classification

Risk Domain

AI Safety & Failures

Risk Subdomain

7.3. Lack of capability or robustness

MIT Risk Profile

AIUnintentionalPost-deployment

Topics

Explainability

Source

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Research Papers on This Topic

Concrete Problems in AI Safety

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1,178 citationsJul 2021

Mitigation Tools

Tools related to this risk domain, via shared responsible AI topics.

Open Source

lime

marcotcr/lime

Lime: Explaining the predictions of any machine learning classifier

⭐ 12,100

facets

PAIR-code/facets

Visualizations for machine learning datasets

⭐ 7,374

interpret

interpretml/interpret

Fit interpretable models. Explain blackbox machine learning.

⭐ 6,788

captum

Source Reports(1 total)

The English test that ruined thousands of lives

bbc.com· Feb 2022

AI Incident Database (AIID), MIT AI Risk Initiative. Data from the AI Incident Database (AIID), MIT AI Risk Initiative. Licensed under CC BY-SA 4.0.

Databaseaiid

LicenseCC BY-SA 4.0

Dec 2015

pytorch/captum

Model interpretability and understanding for PyTorch

⭐ 5,548

Commercial

Datomize

Synthetic data generation for privacy compliance

Protect AI

Helping organizations build and use AI safely.

Censius

AI observability for model governance.

HiddenLayer

Model detection and response (MDR) for AI model protection.