Explainable AI with Complete Audit Trails
Make your AI hiring decisions transparent and accountable. Our XAI layer provides detailed explanations for every decision, complete provenance tracking, and immutable audit trails.
Decision Explanation
Python, ML, Data Analysis matched requirements
7 years in relevant roles (required: 5+)
MS in Computer Science from accredited university
Black Box AI is a Compliance Risk
Regulators and candidates demand transparency. Unexplainable AI decisions lead to lawsuits and regulatory penalties.
of candidates want to know why AI rejected them
LL144 requires explainability and audit trails
AI Act mandates transparency for high-risk systems
Three Layers of Explainability
From high-level summaries to technical deep dives
Decision Summary
Plain-language explanation of why the AI made its decision. Perfect for candidates and HR teams.
Example:
"Candidate recommended due to strong skills match (87%), relevant experience (7 years), and educational background."
Feature Attribution
Detailed breakdown of which factors influenced the decision and by how much. SHAP values and feature importance.
Shows:
- • Skills: +35 points
- • Experience: +28 points
- • Education: +24 points
Complete Provenance
Full audit trail from data input to final decision. Every step logged with timestamps and version control.
Tracks:
- • Data sources
- • Model versions
- • Processing steps
- • Decision logic
Enterprise-Grade Explainability
Human-Readable Explanations
Plain language summaries anyone can understand
SHAP Values
Industry-standard feature attribution analysis
Decision Trees
Visual representation of decision logic
Audit Logs
Immutable record of every decision
Cryptographic Verification
Tamper-proof audit trails with blockchain
Compliance Ready
Meets NYC LL144 and EU AI Act requirements
Who Needs XAI?
Explainable AI benefits everyone in the hiring process
For Employers
Regulatory Compliance
Meet transparency requirements for NYC LL144, EU AI Act, and EEOC
Legal Protection
Defend against discrimination lawsuits with complete audit trails
Trust & Accountability
Build trust with candidates and stakeholders through transparency
AI Debugging
Identify and fix issues in your AI hiring tools
For Candidates
Understand Decisions
Know exactly why you were selected or rejected
Improve Applications
Get actionable feedback on how to strengthen your profile
Fair Treatment
Verify that decisions were made fairly and without bias
Right to Explanation
Exercise your legal right to understand automated decisions
Industry-Standard XAI Methods
Explainability Techniques
SHAP (SHapley Additive exPlanations)
Game-theory based approach to explain individual predictions
LIME (Local Interpretable Model-agnostic Explanations)
Local approximations to explain complex model decisions
Feature Importance
Quantify which features matter most for each decision
Counterfactual Explanations
"What would need to change for a different outcome?"
Audit Trail Features
Immutable Logging
Cryptographically secured logs that cannot be altered
Version Control
Track which model version made each decision
Data Provenance
Complete lineage from input data to final output
Timestamp Verification
Precise timestamps for every step in the process