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Towards more holistic interpretability: A lightweight disentangled Concept Bottleneck Model

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📄 Abstract

Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by predicting human-understandable concepts as intermediate representations. However, existing CBMs often suffer from input-to-concept mapping bias and limited controllability, which restricts their practical value, directly damage the responsibility of strategy from concept-based methods. We propose a lightweight Disentangled Concept Bottleneck Model (LDCBM) that automatically groups visual features into semantically meaningful components without region annotation. By introducing a filter grouping loss and joint concept supervision, our method improves the alignment between visual patterns and concepts, enabling more transparent and robust decision-making. Notably, Experiments on three diverse datasets demonstrate that LDCBM achieves higher concept and class accuracy, outperforming previous CBMs in both interpretability and classification performance. By grounding concepts in visual evidence, our method overcomes a fundamental limitation of prior models and enhances the reliability of interpretable AI.

Authors (3)

Gaoxiang Huang

Songning Lai

Yutao Yue

Submitted

October 17, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

This paper introduces the Lightweight Disentangled Concept Bottleneck Model (LDCBM) to address limitations in existing Concept Bottleneck Models (CBMs), such as input-to-concept bias and limited controllability. LDCBM automatically groups visual features into semantically meaningful components without requiring region annotations, improving the alignment between visual patterns and concepts through a filter grouping loss and joint concept supervision. This leads to more transparent, robust decision-making and superior concept and class accuracy.

Business Value

Enhances trust and accountability in AI systems by providing more transparent and robust decision-making, crucial for applications where understanding the 'why' behind a prediction is critical.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Moderate. Requires careful integration into existing ML pipelines, but the method itself is designed to be lightweight.

Limitations Addressed

Input-to-concept mapping bias in CBMs,Limited controllability of CBMs,Lack of automatic feature grouping,Damage to responsibility of concept-based methods

Performance Gains

Achieves higher concept and class accuracy, outperforming previous CBMs in both interpretability and classification performance.

Technical Tags

Concept Bottleneck ModelsInterpretabilityDisentangled RepresentationsFeature GroupingSupervised LearningDeep LearningComputer VisionClassification

Research Topics

Explainable AI (XAI)Interpretable Machine LearningConcept-based ModelsRepresentation LearningModel Bias

Methods & Architectures

Disentangled Concept Bottleneck Model (LDCBM)Filter grouping lossJoint concept supervisionDeep learning Concept Bottleneck Models (CBMs)Disentangled Concept Bottleneck Model (LDCBM)

Applications & Tasks

Computer Vision Machine Learning Interpretability Decision Support Systems Improving model interpretabilityReducing input-to-concept biasEnhancing controllability of CBMsRobust decision-making Image classificationConcept predictionModel explanation

Datasets & Benchmarks

Datasets

three diverse datasets

Concept accuracyClass accuracyInterpretabilityClassification performance

Related Fields

Explainable AI (XAI)Machine LearningComputer VisionRepresentation Learning

Keywords

interpretabilityconcept bottleneck modelsdisentangled representationsexplainable AIfeature learningdeep learningcomputer visionclassificationbiascontrollabilityfilter groupingsupervised learning

Academic Context

#Explainable AI (XAI)#Interpretable Machine Learning#Concept-based Models#Representation Learning#Model Bias

Commercial Potential

Potential Products

Interpretable AI librariesExplainable computer vision modulesDecision support tools with built-in explanations

Target Industries

HealthcareFinanceAutonomous SystemsAI Development

Use Case Examples

Providing explanations for medical image diagnoses.Understanding why a financial model flags a transaction as fraudulent.Debugging and improving the reliability of image classification systems.

Competitive Edge

Improves upon existing CBMs by offering better disentanglement, reduced bias, and enhanced controllability, leading to superior performance in both interpretability and task accuracy.

Market Opportunity

Strong and growing demand for interpretable and trustworthy AI solutions.

Revenue Models

Licensing of the LDCBM technologyintegration into AI platformsconsulting services for explainable AI.

Resource Requirements

Compute Needs

Moderate, typical for deep learning models trained on image datasets.

Data Requirements

Labeled image datasets suitable for classification tasks, potentially with concept annotations.

Deployment Constraints

Requires careful integration and validation to ensure the learned concepts are truly meaningful and aligned with human understanding.

Scalability

The 'lightweight' nature suggests good scalability, but performance on extremely large datasets would need further study.

Regulatory Considerations

Compliance with AI explainability regulations (e.g.GDPR's right to explanation).

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into specialized products.

Patent Potential

Moderate

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