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Algorithmic hiring tools are under intense legal and regulatory scrutiny. EEOC and state laws increasingly require demonstrable fairness in automated employment decisions.
EEOC Scrutiny
The EEOC is actively investigating AI hiring bias. Non-compliant tools create massive liability.
Proxy Discrimination
Even "neutral" variables can encode bias. Zip codes proxy race. Names proxy gender.
NYC Local Law 144
First of many laws requiring algorithmic bias audits. More jurisdictions following.
DEI Accountability
Boards and investors demand diversity progress. Black box tools undermine efforts.
Aequitas provides the documented reasoning you need to argue a hiring decision is job-related and defensible.
Proxy Detection Engine
Identifies variables that correlate with protected characteristics before they influence decisions.
Disparate Impact Analysis
Continuous monitoring for adverse impact across all protected groups.
Documented Decision Trails
Decision audit trails written for your own EEOC or legal review.
SEE IT IN ACTION
A worked example: a hiring decision traced from screening model to certificate, with job-related criteria isolated from proxies along the way.
HR & RECRUITING
Illustrative sampleDefensible hiring AI
Reasoning is traceable, formally verified, and the independent audit passed.
SHA-256 04a056e5b1d26f1a…
Illustrative example; the hash is a real SHA-256 digest of this sample’s content.
IN PRACTICE
Fair automated resume review with explainable decisions.
BEFORE
Screening algorithms perpetuate historical bias in hiring patterns
AFTER
Every screening decision tied to job-relevant criteria only
Transparent interview assessment with bias alerts.
BEFORE
Inconsistent scoring based on interviewer unconscious bias
AFTER
Structured scoring with real-time bias detection
Fair pay decisions with complete reasoning.
BEFORE
Pay gaps persist due to opaque compensation algorithms
AFTER
Every compensation decision justified by legitimate factors
See how Aequitas certifies a judgment on your own HR material.
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