AIAgentree captures the structured reasoning behind every AI hiring decision — resume screening, candidate scoring, interview evaluation, and offer recommendations. When an AI rejects a candidate, the Decision Context Graph preserves the full normative argument tree: which criteria were evaluated, what weight each carried, which evidence from the application supported or opposed advancement. This is not optional — NYC Local Law 144 requires bias audits for automated employment decisions, EEOC guidelines demand documented evaluation criteria, and EU AI Act classifies hiring AI as high-risk requiring full transparency. The Precedent Flywheel detects adverse impact patterns across decisions with statistical confidence: if AI screening disproportionately rejects candidates from protected groups, AIAgentree surfaces this from decision structure, not from post-hoc analysis. Human override tracking captures when recruiters disagree with AI assessments — these correction events become the most valuable precedent for reducing future bias. 12 semantic elements, immutable traces, every criterion weighted and documented. They tell you the AI scored the candidate low. We tell you exactly which criteria drove the score and whether similar scoring patterns show adverse impact.
Capture WHY your AI screened, ranked, and recommended every candidate — not just the outcome. Structured decision traces with bias detection and complete audit trails for AI hiring.
Best for: Enterprise HR departments, talent acquisition teams, staffing agencies, and HR tech companies deploying AI for resume screening, candidate ranking, and hiring recommendations.
AI resume screening and candidate ranking decisions face increasing legal scrutiny. NYC Local Law 144, EEOC guidance, and EU AI Act all require documentation of AI hiring decisions. Without structured records, every lawsuit is a discovery nightmare.
AI screening tools can develop bias that is invisible without decision-level analysis. Aggregate metrics show pass rates, but without structured traces of individual decisions, you cannot identify which criteria drive disparate outcomes.
Rejected candidates increasingly ask why they were screened out. GDPR Article 22 gives EU candidates the right to contest automated decisions. Without documented reasoning, your only response is "the system decided" — which invites regulatory complaints.
Document every AI hiring criterion, detect bias early, and build legally defensible audit trails.
12 semantic elements capture the full context of every AI screening decision. Skills matched, experience evaluated, criteria weights applied, disqualification reasons, and confidence levels — all structured and searchable for every candidate.
Pattern detection analyzes AI screening outcomes across candidate populations. Statistical confidence scoring surfaces disparities in pass rates, ranking distributions, and advancement recommendations — identifying potential adverse impact before it becomes a legal problem.
Track AI screening outcomes across demographic groups with four-fifths rule calculations and statistical significance testing. Get early warning when AI screening patterns suggest disparate impact on protected classes.
Append-only immutable traces create tamper-evident records for every AI hiring decision. Human overrides are captured with reasoning. The complete audit trail is ready for NYC LL144 audits, EEOC investigations, and litigation discovery.
Screening Criteria
Every AI screening and ranking decision documented with the criteria, weights, and reasoning applied.
Detection Built In
Statistical analysis of AI hiring patterns to catch adverse impact before it becomes a legal problem.
Defensibility
Immutable audit trails ready for NYC LL144, EEOC, EU AI Act, and litigation discovery.
"AI hiring bias is invisible without structured decision records. You can't detect adverse impact from a spreadsheet of scores. You need the full reasoning tree — which criteria, what weights, which evidence — to know if your AI is discriminating."
The most valuable data in AI hiring isn't the AI's decisions — it's where humans override the AI. Every time a recruiter advances a candidate the AI rejected, that correction event teaches the system something no model training can: what your organization actually values versus what the algorithm thinks it should value.
Accountability over opacity. Earned autonomy over granted autonomy.
LLMs love graphs. They hate flat databases. AIAgentree stores decisions as structured argument trees — the format AI models reason about best.
Every relationship is supports or opposes — not generic "related to." LLMs instantly know which evidence argues for or against a decision.
Each decision is a self-contained tree of 10–100 nodes with a natural root — not millions of nodes in a hairball. No graph explosion, no runaway traversal.
Structured 300–600 token chunks extract 120% more relevant information than 8,000-token context windows. Purpose-built for LLM consumption.
Past decisions become first-class argument nodes in new decisions — not vague references. Composable, citable, challengeable institutional memory.
We make AI hiring defensible. We don't replace your ATS or recruiting tools.
Your AI screens candidates. AIAgentree traces why each candidate was scored the way they were — the criteria, weights, and evidence behind every decision.
Use your ATS for pipeline management. AIAgentree sits alongside it, capturing the decision reasoning at each stage gate where AI influences outcomes.
No tool eliminates bias. AIAgentree makes bias visible and measurable through structured decision patterns — so you can detect it, document it, and fix it.
AI hiring tools screen candidates. AIAgentree ensures you can defend every screening decision.
AIAgentree is part of a family of four products that cover the full spectrum of Structured Decision Intelligence — from human deliberation to AI governance.
Human-to-human structured debate. Teams map decisions as pro/con trees with 16 evaluation categories.
Meeting intelligence →Collective AI Intelligence. 7+ LLMs independently argue, then cross-rate — consensus reveals confidence.
Multi-LLM analysis →AI Decision Tracing. Capture WHY AI agents decide — structured audit trails for EU AI Act compliance.
Learn more →AI debate simulations. 9 AI personas argue any topic from every angle — synthetic focus groups in minutes.
AI simulations →AI Agentree creates a structured decision trace for every AI screening and ranking decision — documenting which criteria were applied, how candidates were weighted, what factors influenced the outcome, and the confidence level. This documentation is exactly what legal teams need to demonstrate that AI hiring decisions were based on legitimate, job-related criteria rather than protected characteristics.
Yes. AI Agentree's pattern detection with statistical confidence scoring analyzes decision patterns across thousands of AI hiring decisions. It surfaces disparities in screening rates, ranking distributions, and advancement recommendations across demographic groups — identifying potential adverse impact before it becomes a legal problem.
AI Agentree tracks AI screening and ranking outcomes across candidate populations and applies statistical confidence scoring to detect disparate impact patterns. When AI screening rates differ significantly between groups, the system surfaces this with statistical evidence — enabling HR teams to investigate and correct before adverse impact materializes into regulatory action.
AI Agentree provides the decision tracing infrastructure that supports compliance with NYC Local Law 144 (AI hiring audit requirements), EEOC guidance on AI in employment decisions, EU AI Act requirements for high-risk AI in recruitment, Illinois AI Video Interview Act documentation requirements, and general Title VII adverse impact analysis obligations.
AI Agentree works alongside your existing ATS (Greenhouse, Lever, Workday, iCIMS) and AI screening tools. It integrates with LangChain, n8n, and custom AI agent pipelines via a lightweight SDK with less than 10ms latency overhead. Decision traces are captured without disrupting your existing hiring workflow. Export data via CSV or JSON for integration with HRIS and compliance reporting tools.
Start tracing AI hiring decisions before your next audit or legal challenge.