How does Bookbag help with NYC Local Law 144 compliance?

Local Law 144 requires employers using AI in hiring to conduct annual bias audits and provide candidate notice. Bookbag creates the decision-level evidence trail that bias auditors need — documenting what factors the AI considered, how it scored candidates, and whether patterns of adverse impact exist. The structured audit log serves as supporting documentation for the required annual audit.

Can Bookbag detect proxy discrimination in AI hiring models?

Bookbag's taxonomy includes 'protected characteristic proxy detected' as a failure category. When AI scoring models use features that correlate with protected characteristics — such as zip code (race proxy), graduation year (age proxy), or name-based assumptions — the system flags these for review. The evidence trail documents exactly which features contributed to each decision.

How does this work with existing ATS platforms?

Bookbag integrates downstream of your AI screening tool. When the AI produces a candidate recommendation, that recommendation and its supporting evidence are submitted to Bookbag for structured evaluation. Bookbag doesn't replace your ATS — it adds the audit and bias-testing layer that employment law increasingly requires.

HR & Hiring

Audit AI Hiring Decisions for Fairness and Legal Compliance

Ensure AI-driven resume screening, candidate scoring, and employment decisions are bias-tested and legally defensible.

Title VIIEEOC guidelinesNYC Local Law 144Illinois AI Video Interview Act (AIPA)Colorado AI ActADAADEA

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The Problem

Employers are using AI to screen resumes, score candidates, conduct video interviews, and even make termination recommendations. The scale is impressive — but so is the legal exposure. NYC Local Law 144 requires annual bias audits for AI hiring tools. Illinois requires consent for AI video interviews. Colorado's AI Act mandates impact assessments. The EEOC has made clear that employers — not AI vendors — are liable for discriminatory hiring decisions, regardless of whether an algorithm made the call.

AI resume screening may systematically disadvantage protected classes
Candidate scoring models lack explainable rationale for individual decisions
No structured documentation for NYC Local Law 144 bias audit requirements
Adverse impact analysis requires decision-level data most AI tools don't produce

Evidence Payload

evidence

Position: Senior Software Engineer. Candidate: 8 years experience, BS Computer Science, 3 relevant certifications, emplo...

policy_context

Job requirements: 5+ years experience, BS in CS or equivalent, cloud platform certification preferred. EEOC selection gu...

ai_generated_content

RECOMMENDATION: DO NOT ADVANCE — Overall score: 74/100. Culture fit below threshold (minimum 75). Skills assessment stro...

What Gets Submitted

What gets submitted when an AI hiring decision is audited

evidence

Position: Senior Software Engineer. Candidate: 8 years experience, BS Computer Science, 3 relevant certifications, employment gap (2021-2022). Skills match: 87%. Culture fit score: 72%. Interview panel: 3 of 4 recommended advance.

policy_context

Job requirements: 5+ years experience, BS in CS or equivalent, cloud platform certification preferred. EEOC selection guidelines: 4/5ths rule for adverse impact. NYC LL 144: annual bias audit required. Company policy: employment gaps cannot be used as negative factor.

ai_generated_content

RECOMMENDATION: DO NOT ADVANCE — Overall score: 74/100. Culture fit below threshold (minimum 75). Skills assessment strong but weighted score reduced by experience consistency factor.

model_trace

Resume parsing → skills extraction → experience scoring → culture fit model → gap analysis → composite score → threshold comparison → recommendation

model_metadata

model: candidate-scoring-v3.1, confidence: 0.77, features_used: 18, bias_audit_date: 2024-01-15, four_fifths_status: compliant

redacted_fields

candidate_name, email, phone, address, age_indicators, photo

How the Gate Works

Step 1

Submit Evidence

AI decision + evidence payload submitted for structured evaluation

Step 2

Review Against Policy

Decision evaluated against HR & Hiring regulations and policy context

Step 3

Verdict & Audit Trail

Structured verdict with failure categories, corrections, and immutable audit record

Evaluation Taxonomy

Failure Categories

Protected characteristic proxy detected
Employment gap penalty (policy violation)
Culture fit score lacks objective basis
Adverse impact indicator
Missing required disclosure to candidate
Scoring criteria not job-related

Business Impact

EEOC complaint
State AG investigation
NYC LL 144 violation
Class action employment discrimination
Candidate trust and employer brand damage

Evidence Sufficiency

Complete application with scoring breakdown
Partial scoring — missing criteria weights
Critical scoring factor undocumented
Scoring conflicts with stated job requirements

Example Verdict

verdict: blocked decision_type: candidate_screening failure_categories: [gap_penalty, culture_fit_subjective] primary_failure: gap_penalty severity: critical business_impact: eeoc_complaint_risk EVIDENCE REVIEW experience: 8 years (exceeds 5-year req) ✓ education: BS CS ✓ certifications: 3 relevant ✓ skills_match: 87% (strong) ✓ interview_panel: 3/4 recommend advance ✓ culture_fit: 72% (below 75% threshold) FINDING "AI recommendation contradicts panel assessment. 'Experience consistency factor' penalizes 2021-2022 employment gap — violates company policy prohibiting gap-based scoring. 'Culture fit' score (72%) lacks objective, job-related criteria per EEOC Uniform Guidelines. Candidate exceeds all stated requirements." CORRECTED RECOMMENDATION "ADVANCE — Candidate meets or exceeds all stated requirements. Remove gap penalty per company policy. Culture fit assessment requires objective criteria before use in screening decisions." AUDIT TRAIL reviewer: sme_hr_legal_4521 reviewed_at: 2024-07-01T10:28:44Z policy_version: hiring-2024-q3 bias_audit: current (LL144 compliant) escalation: mandatory (adverse recommendation contradicting panel)

Compliance Frameworks

Title VIIEEOC guidelinesNYC Local Law 144Illinois AI Video Interview Act (AIPA)Colorado AI ActADAADEA

Frequently Asked Questions

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