Enhancing concept alignment with explanatory interactive disentangled representation learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Deep learning models often lack interpretability due to their black-box nature.
• Disentangled representation learning aims to improve model explainability by separating representations based on human-defined concepts.
• Traditional methods require extensive manual labeling, which is impractical for large datasets.

Purpose of the Study:

• To propose the eXplanatory Interactive Disentangled Representation Learning (XIDRL) framework for efficient collaboration between AI techniques and human experts.
• To develop a visual analytics system for exploring concept alignments and refining model behaviors.
• To enhance model interpretability and enable human-controllable disentangled representations.

Main Methods:

• Developed the XIDRL framework integrating a novel SCL+IRM algorithm for improved representation disentangling and concept alignment.
• Designed a visual analytics system to assist experts in understanding model behavior and concept relationships.
• Incorporated the w-BiLRP algorithm to further boost model interpretability.

Main Results:

• The SCL+IRM algorithm demonstrated enhanced alignment capabilities for disentangled representations.
• The visual analytics system facilitated exploration of concept alignments and model comprehension.
• The XIDRL framework successfully enabled the creation of interpretable and human-controllable disentangled representations, as shown in case studies.

Conclusions:

• The XIDRL framework offers an effective approach for creating interpretable AI by integrating advanced disentangled representation learning with human expertise.
• The developed system and algorithms provide practical tools for machine learning experts to enhance model explainability and control.
• Future work includes releasing code, data, and model checkpoints to facilitate further research and application.