TrustLens Documentation

TrustLens helps ML teams answer a practical question with evidence: should this model be deployed, delayed, or blocked?

This documentation is organized around real workflows used by ML engineers, not around internal package structure.

Start Here

• New to TrustLens: read Getting Started

• Need context first: read Overview and The Problem

• Need API details: open API Reference

I Want To…

• Evaluate one model quickly: Getting Started

• Understand score logic and verdict rules: Trust Score Explained

• Investigate reliability and risk dimensions: Features and Modules

• Use TrustLens in production workflows: Guides

• Understand what is and is not supported: Known Limitations

• Extend the system safely: Architecture, Contributing

Documentation Map

Foundations

• Overview

• The Problem

• Audience

Concepts

• Features and Modules

• Trust Score Explained

• Known Limitations

Practical Guides

• Model Comparison Workflow

• CI and Deployment Gate Workflow

• Fairness Audit Workflow

Reference

• API Reference

• Metric: Calibration

• Metric: Failure

• Metric: Bias

• Metric: Representation

Project and Contribution

• Architecture

• Design Principles

• Experimental Modules

• Contributing Guide

• Roadmap

• Code of Conduct

Documentation

• Getting Started
  • Framework Support
  • Installation
  • Minimal Working Example
  • What happened?
  • Next Steps
• Overview
  • Why This Matters
  • What TrustLens Evaluates
  • Typical Workflow
  • What You Get
  • Related Pages
• The Problem Nobody Ships Around
  • Why standard metrics fall short
• Who This Is For
  • ML Engineers
  • Data Scientists
  • Researchers
  • AI Governance & Compliance Teams
  • Prerequisites
• Features and Modules
  • Calibration Module
  • Failure Analysis Module
  • Bias and Fairness Module
  • Representation Module
  • Trust Scoring Engine
• Real-World Use Cases
  • How to Read These Examples
  • Safety-Critical Model Selection
  • Governance and Fairness Review
  • Post-Deployment Reliability Monitoring
  • Candidate Ranking for Release
  • Related Pages
• Trust Score Explained
  • Why a Composite Score Exists
  • Inputs Used
  • Scoring Workflow
  • Default Weights
  • Penalties
  • Deployment Blockers
  • Grade Interpretation
  • How to Use This in Practice
  • Related Reading
• Known Limitations
  • Scope
  • Probability Dependency
  • Dataset Size Effects
  • Fairness Constraints
  • Representation Constraints
  • Threshold and Penalty Design
  • Not a Causal Fairness Auditor
  • Recommended Mitigations
• Model Comparison Workflow
  • When to Use This
  • Workflow
  • Example
  • Decision Checklist
  • Recommended Output Artifacts
• CI and Deployment Gate Workflow
  • Goal
  • Basic Approach
  • Example Policy
  • Minimal Pattern
  • Operational Recommendations
• Fairness Audit Workflow
  • Goal
  • Inputs Needed
  • Workflow
  • Example
  • Interpretation Checklist
  • Remediation Options
• API Reference
  • Bias and Fairness Metrics
  • Calibration Metrics
  • Failure Analysis Metrics
  • Representation Metrics
  • Core API
  • Visualization
• System Architecture
  • Why This Design Matters
  • High-Level Data Flow
  • Component Interactions
  • Execution Sequence
  • Layer Responsibilities
  • Extensibility Path
  • Architectural Constraints
  • Related Pages
• TrustLens Design Principles
  • 1. Simplicity > Complexity
  • 2. Modular by Design
  • 3. Visual-First Outputs
  • 4. Research + Practical Balance
  • 5. Extensibility Without Fragility
  • 6. Test Everything
  • 7. Documentation as a Feature
  • 8. Performance is Not Optional
• Experimental Modules
  • Current Experimental Modules
  • Experimental Promotion Criteria
  • Contributing to Experimental
• TrustLens — Future Extensions
  • 1. Web Dashboard
  • 2. Public Leaderboard
  • 3. Hugging Face Integration
  • 4. Benchmarking Suite
  • 5. Model Monitoring Integration
  • 6. Plugin Marketplace
  • 7. Interactive Learning Mode
• Documentation Page Template
  • Title
  • Why This Matters
  • When to Use
  • When Not to Use
  • Inputs and Assumptions
  • Output and Interpretation
  • Minimal Example
  • Limitations and Caveats
  • Related Pages
• Internal RFC: Prediction Semantics Contract
  • 1. Classification Semantics
  • 2. Regression Semantics
  • 3. Label Consistency
  • 4. Metadata Hooks
  • 5. Manual Overrides (model=None)
  • 6. Degraded Mode Transparency
• Implementation plan: XGBoost
  • Executive summary
  • Prerequisites (shared with other backends)
  • Objectives (XGBoost)
  • Non-goals (v1)
  • Technical design
  • Files to add or change (checklist)
  • Testing strategy
  • Acceptance criteria (XGBoost complete)
  • Suggested PR breakdown
  • Maintainer sign-off
  • FAQ
  • Document history
• Implementation plan: Keras
  • Scope: “Keras” vs “TensorFlow” in this repo
  • Executive summary
  • Prerequisites
  • Objectives (Keras)
  • Non-goals (v1)
  • Technical design
  • Files to add or change (checklist)
  • Testing strategy
  • CI recommendations
  • Acceptance criteria (Keras experimental “ready”)
  • Suggested PR breakdown
  • Promotion to stable API
  • FAQ
  • Document history
• Implementation plan: TensorFlow
  • Executive summary
  • Prerequisites
  • Objectives (TensorFlow)
  • Non-goals (v1)
  • Technical design
  • Files to add or change (checklist)
  • Testing strategy
  • Acceptance criteria (TensorFlow track “ready”)
  • Suggested PR breakdown
  • Security and maintenance
  • Maintainer sign-off
  • FAQ
  • Document history

Indices and Tables

• Index

• Module Index

• Search Page