AI Incident Intelligence

iTutorGroup automated software rejected older applicants

The U.S. EEOC alleged that iTutorGroup programmed application software to automatically reject female applicants aged 55 or older and male applicants aged 60 or older, affecting more than 200 qualified applicants.

highiTutorGroupautomated decisionUnited States

Impact

The case resulted in a $365,000 settlement and injunctive relief.

Contributing factors

Age and sex criteria were encoded directly into automated screening software.

Response

Five-year consent decree, monetary relief and restrictions on discriminatory screening.

Assurance lesson

This record should inform control design, testing and monitoring for comparable AI systems. The incident database does not infer that every system using the same provider or model shares the same failure.

Control lessons

Assurance controls implicated by this incident pattern.

These are CRG methodology mappings from the documented incident to controls worth testing in comparable systems. They do not assert that any single control would have prevented the incident.

RISK-04

Impact assessment

Material impacts on users, workers, customers or the public are assessed.

FAIR-01

Affected-group analysis

Materially affected groups and differential risks are identified.

FAIR-02

Bias and disparity evaluation

Where relevant, outcome disparities are measured using appropriate metrics.

FAIR-03

Human contestability

People can challenge or escalate consequential AI-supported outcomes where appropriate.

Evidence

Source provenance is part of the incident record.

U.S. Equal Employment Opportunity Commission · confidence 99% · last verified 23 Aug 2026

Open underlying source