Eightfold scored job applicants on a hidden scale and discarded the low ones before a human ever looked, allegedly without the disclosures the law requires.
A proposed class action alleges Eightfold’s platform compiled data on more than a billion workers, scored applicants from zero to five, and filtered out low-ranked candidates before any human review, all without the consent and disclosure the Fair Credit Reporting Act mandates for consumer reports. Unlike most AI hiring suits, this one does not claim the algorithm was biased. It claims the algorithm was secret, a new legal theory, brought by a former EEOC chair, that reframes opaque scoring itself as the violation.
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Eightfold’s exposure is not bias, it is opacity. Applicants were scored and rejected with no explanation and, allegedly, no disclosure. AVAAS measures the Explainability Gap and generates individual-level causal explanations of why each applicant was scored as they were, the auditable record any transparency obligation demands.
This entry is one of 37 documented cases in the AVAAS evidence ledger, a public record of AI and automated-system failures with a verified source on every entry.
Every case here reached a person.
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