Source-linked AI summary
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
Thilo Spinner, Udo Schlegel, Hanna Schäfer, Mennatallah El-Assady
TL;DR
Interactive and explainable machine learning needs a practical way to understand models, diagnose limitations, and refine them. The paper proposes a monitored iterative framework and operationalizes it in explAIner within TensorBoard. A qualitative study with nine participants found the integrated workflow useful while identifying opportunities for extension.
Problem
Existing XAI tools and conceptual work leave a gap between theoretical workflows and practical integration into active machine learning development and debugging.
Method
The paper combines an iterative three-stage XAI pipeline with global monitoring and steering mechanisms, implementing it as explAIner in TensorBoard.
Results
A qualitative user-study with nine participants across expertise levels found the implemented workflow useful for analyzing the tool and informing future versions.
Takeaways & Limitations
The integrated framework and system support an informed machine learning process while exposing opportunities for further extensions.
Takeaways & Limitations
TensorBoard imposed limitations on the user interface, data handling, and sharing across plugin tabs, complicating mutual views and closing the development-analysis loop.
Abstract
from arXiv · showhide
We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different explainable AI methods; as well as (3) refine and optimize the models. Our framework combines an iterative XAI pipeline with eight global monitoring and steering mechanisms, including quality monitoring, provenance tracking, model comparison, and trust building. To operationalize the framework, we present explAIner, a visual analytics system for interactive and explainable machine learning that instantiates all phases of the suggested pipeline within the commonly used TensorBoard environment. We performed a user-study with nine participants across different expertise levels to examine their perception of our workflow and to collect suggestions to fill the gap between our system and framework. The evaluation confirms that our tightly integrated system leads to an informed machine learning process while disclosing opportunities for further extensions.
1 INTRODUCTION
Deep learning improves performance but remains difficult to understand, creating costly development cycles and trust challenges. The paper addresses this gap with an integrated framework, system, and user-study.
- Deep learning models can improve performance while remaining black boxes that make development time-consuming and trial-and-error driven.
- Explainable AI provides human-readable and interpretable explanations for decisions made by otherwise opaque algorithms.
- Visual analytics supports interactive, mixed-initiative analysis that bridges differences between user knowledge and explainable AI insights.
- Prior work often lacked a tightly integrated practical framework connecting interactive, interpretable, and explainable machine learning.
- The paper proposes a generalizable workflow, implements it as explAIner, and evaluates it with participants spanning different expertise levels.
2 RELATED WORK
Prior XAI, interactive machine learning, and visual analytics research offers specialized methods and workflows but lacks a broadly applicable, operationally integrated pipeline. The paper combines these strands with global monitoring and steering mechanisms.
- Reviewed XAI methods vary in data coverage, model abstraction, required dependencies, and implementation characteristics.
- Existing conceptual work identifies gaps in formal definitions, real-world applications, explanations for novices, and global monitoring of explainability processes.
- Explainable AI: LIME and ANCHORS generate local, high-abstraction, model-agnostic explanations from model inputs and outputs.
- Explainable AI: Saliency maps and Concept Activation Vectors provide model-specific explanations at different abstraction levels, while low-abstraction methods expose filters or graph dataflow.
- Interactive Machine Learning and Visual Analytics: Visual analytics systems support model understanding, diagnosis, and refinement through specialized inspections, feature analyses, model comparisons, and iterative interventions.
- Interactive Machine Learning and Visual Analytics: The proposed pipeline covers different pathways across these tasks and uses global monitoring and steering mechanisms to guide the overall process.
3 CONCEPTUAL FRAMEWORK
The framework operationalizes interactive and explainable machine learning as an iterative pipeline for understanding, diagnosing, and refining models, supported by global monitoring and steering mechanisms. Explainers produce visual, verbal, surrogate-model, or model-transition outputs across model states and explainer types.
- Conceptual Framework: The framework targets practical operationalization by integrating theoretical concepts with implementation needs without limiting the workflow to particular software or hardware.Its stated design priorities are practicability, completeness, and full coverage.
- XAI Pipeline: The XAI pipeline structures model development as iterative understanding, diagnosis, and refinement across successive model states.Explainers generate insights or identify flaws, while refinements induce transitions to new model states.
- XAI Pipeline: Explainers interact with models to produce explanations or transition functions that support diagnosis and model refinement.Explanations can be visualizations, verbalizations, or surrogate models; transition functions produce new model states.
- XAI Pipeline: Explainers are categorized by model-state inputs, explainer level, abstraction, and dependencies on data, models, or domain knowledge.Global explainers consider all data inputs and outputs, whereas local explainers use a subset or sample; abstraction ranges from part of a model to the whole model.
- XAI Pipeline: Multi-model explainers compare two or more model states to support comparative visualization, model selection, or further state transitions.The example pipeline uses five explainers and three model states, including comparison between the original and refined states.
- Global Monitoring and Steering Mechanisms: The framework proposes global mechanisms to guide, steer, and track the XAI pipeline throughout its phases.These mechanisms include monitoring and steering functions, while the framework describes eight mechanism groups and supports provenance and quality tracking.
4 SYSTEM DESIGN AND IMPLEMENTATION
explAIner operationalizes an interactive XAI workflow in TensorBoard through dedicated views for understanding, diagnosis, refinement, and reporting. Its implementation combines graph-based model inspection, runtime explainers, provenance tracking, and reporting features to support iterative analysis.
- The system embeds explainers into the visual analytics workflow and executes them at run-time using model or native TensorBoard backends.Model-input/output explainers use an external model backend, while graph-tensor explanations use the native plugin backend.
- The implementation follows TensorBoard’s plugin architecture and design conventions, while manually adding cross-plugin provenance functionality.The plugin uses API, backend, and frontend layers and stores graph definitions, tensors, and models through explAIner summaries.
- explAIner extends TensorBoard with four dashboard views covering the XAI pipeline stages and global monitoring.The views support understanding, diagnosis, refinement, and reporting.
- Understanding: The understanding view helps users inspect model architecture through interactive graph visualizations and information cards.It provides model developers with layer sizes, loss functions, and optimizers, while explaining model functionality to users and novices.
- Diagnosis: The diagnosis view combines graph selection, explainers with different abstraction levels, overlaying explanation cards, and supplementary interpretation information.High-abstraction explainers operate on a selected sample and model, whereas low-abstraction explainers analyze graph entities or subsets.
- Reporting: Provenance cards persist findings, annotations, decisions, and iterative feedback across the workflow to support reporting, justification, and reproducibility.The reporting phase arranges cards from understanding, diagnosis, and refinement into a documented storytelling process.
5 EVALUATION
The qualitative evaluation examined explAIner’s workflow and usability with nine participants across model-novice, model-user, and model-developer groups. Participants generally valued the system, while identifying needs for guidance, additional explainers, comparison tools, and architectural changes.
- Study Design: Nine participants completed a qualitative pair-analytics study using simple and complex MNIST networks across novice, user, and developer groups.Participants worked through understanding, diagnosis, and refinement tasks while retaining the ability to loop between them.
- Expectations: Participants’ expectations aligned with model-quality monitoring, search-space exploration, reporting and trust building, and knowledge generation.Users also wanted to verify pretrained models with XAI methods, which the proposed pipeline supports.
- User Feedback: Participants requested data inspection, convergence metrics, and additional explainers, while model developers valued diagnosis more than model users and novices.These requests included explainers for dataset balance and other model properties not implemented in the system.
- User Feedback: Trust in explainer outputs remained an issue, motivating stronger guidance, additional descriptions, and further low-abstraction explainers.Participants also requested standard metrics and dynamic visualizations explaining current outputs, such as LIME surrogate architectures or layer-wise LRP relevance.
- Usability: All participants gave very positive feedback on the system’s overall usability and value, although half considered it too complex for beginners.Suggested remedies included tutorials, suggestions, checklists, labels, and example models.
- Lessons Learned and Future Work: The most requested future functionality included simplified graph presentation, direct comparative analytics, speculative refinement execution, and richer provenance-related interactions.Some suggestions were easy to integrate, whereas on-demand refinement was more difficult within the TensorBoard-based implementation.
6 CONCLUSION
The paper presents explAIner as a framework and system for iterative interactive and explainable machine learning. A nine-participant user study found the system intuitive and helpful for integration into daily workflows.
- Conclusion: explAIner defines an iterative XAI pipeline covering model understanding, diagnosis, and refinement, supplemented by global monitoring and steering mechanisms.The implementation includes provenance tracking and reporting within a TensorBoard-based system.
- Conclusion: A user study with nine participants from different user groups found the system intuitive, helpful, and suitable for integration into daily workflows.The study evaluated usability and usefulness across the represented target groups.