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Incident intelligence/SS-IR-006CASE FILE OPEN
Symbolic editorial illustration for SS-IR-006SERVANTSTACK // INCIDENT INTELLIGENCEFORENSIC IMAGE // VERIFIED FRAME
SS-IR-006 // INCIDENT REPORTReported

Amazon's AI Recruiting Tool

Systematic Gender Discrimination

EXECUTIVE BRIEF

Amazon built an AI recruiting tool to automate resume screening.

BIAS & ACCESS
FAILURE CHAINTRACE COMPLETE
  1. 01TRIGGERAmazon built an AI recruiting tool to automate resume screening.
  2. 02MACHINE ACTIONMaterial contributor
  3. 03MISSING GATENamed SME review and decision audit trail
  4. 04IMPACTRights & due process
01 // INCIDENT SUMMARY

The short version

Amazon built an AI recruiting tool to automate resume screening.

02 // KEY FACTS

Case telemetry

INCIDENT
SS-IR-006
DATE
2014-2017
SYSTEM
Amazon's AI Recruiting Tool
LOCATION / SCOPE
Seattle, USA
EVIDENCE
Reported
AI ROLE
Material contributor
HARM
Rights & due process
SOURCES
1 cited record
03ENTRY POINT // WHAT HAPPENED

The event

Amazon built an AI recruiting tool to automate resume screening. The system was trained on 10 years of hiring data - which reflected Amazon's historically male-dominated engineering workforce. The algorithm learned to penalize resumes containing the word "women's" (as in "women's chess club captain") and downgraded graduates of all-women's colleges. It systematically filtered out qualified female candidates.

04CAUSAL TRACE // AI'S ACTUAL ROLE

What the machine did

The system rated candidates on a 1-5 star scale with no human review before rejection. Resumes mentioning women's organizations, women's colleges, or female-coded language were automatically scored lower. Amazon's own engineers discovered the bias but could not fix it - the model kept finding new proxies for gender.

Material contributorAutomation was a causal participant—not a decorative label for the system around it.
05BLAST RADIUS // CONSEQUENCES

Where the failure landed

Unknown number of qualified women rejected from Amazon jobs based on gender-biased AI scoring. The tool was used for screening technical roles from 2014-2017 before being scrapped. The incident became a landmark case study in AI hiring bias.

06 // EVIDENCE STATUS

Reported

Documented in the cited public record. Follow the sources for the precise evidentiary posture.

SOURCE RECORD UPDATED 2026-07-09

07 // SOURCE LEDGER

1 cited record

  1. 01
08CONTROL FAILURE // MISSING GOVERNANCE

Named SME review and decision audit trail

The failure pattern in this case: Automated judgment without accountable review.

09INTERVENTION POINT // HUMAN IN THE MIDDLE

The moment the path could change

A qualified reviewer tests the basis, context, and disparate impact before the decision reaches a person.

AI PROPOSES→HUMAN OWNS THE DECISION→SYSTEM EXECUTES
10CONTROL DEPLOYMENT // AUTHORITYGATE

SME routing · decision audit trail

AuthorityGate requires training data auditing by HR and DEI SMEs before deploying any AI in hiring decisions. A human expert reviewing the training data would have immediately flagged that 10 years of male-dominated hiring history would encode gender bias. The framework also mandates outcome auditing - comparing AI decisions against demographic baselines to catch bias that survives pre-deployment testing.

RELEVANT KEYSTONE CONTROLHuman-in-the-Loop ValidationHow high-risk actions route to a named subject-matter expert who owns the go or no-go decision.
12 // THE ALTERNATIVE

Autonomy is a design choice.

See the operating model that keeps AI useful while preserving human authority at consequential moments.

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