explAIner

explAIner provides a visual analytics framework that operationalizes an iterative eXplainable AI (XAI) pipeline to improve interpretability, diagnosis, refinement, and monitoring of machine learning models within the TensorBoard environment.

Key Features:

• Interactive Understanding: Enables interactive exploration of model behavior and decision-making using XAI methods.
• Diagnosis of Model Limitations: Incorporates explainable AI methods to identify and diagnose model limitations.
• Model Refinement and Optimization: Supports iterative refinement and optimization of models based on insights from XAI analyses.
• Comprehensive Monitoring Mechanisms: Implements eight global monitoring and steering mechanisms, including quality monitoring, provenance tracking, model comparison, and trust building.
• Integration with TensorBoard: Operates within the TensorBoard environment to integrate XAI analyses with existing model tracking workflows.

Scientific Applications:

• Critical decision-making domains: Applied where AI models require transparency and accountability to support decisions in sensitive or high-stakes contexts.
• Model development workflows: Supports iterative model understanding, diagnosis, refinement, and monitoring during model development and evaluation.

Methodology:

Employs an iterative XAI pipeline that integrates phases of model understanding, diagnosis, refinement, and optimization, supported by a suite of eight global monitoring and steering mechanisms and integrated into TensorBoard.