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Permanent record · RIR–2016

Designing Intrinsically Interpretable Neural Network Architectures via Adaptive Routing and Temporal Diagnostics

Current human-centric XAI relies on post-hoc explainers that often show systematic disagreement when applied to black-box models. This study proposes shifting toward interpretable-by-design neural networks using adaptive routing and temporal diagnostics to improve consistency.

Open to researchQualified 88/100P4 provenance
Primary research question

How do adaptive routing and temporal diagnostics influence the interpretability and accuracy of neural networks?

Knowledge gap

What remains worth asking

The source suggests that post-hoc explainers are limited by inconsistency, and it remains useful to test whether interpretable-by-design architectures can outperform them in real-time tasks.

Potential contribution

Why it may matter

Developing intrinsically interpretable models could fundamentally improve trust and reliability in high-stakes deep learning applications.

Academic placement

OECD fields and topic tags

2.2Electrical, electronic and information engineering
Computer ScienceArtificial IntelligenceHuman-Computer Interaction

Scope: Deep learning models for predictive tasks in healthcare or education. · Method signals: Neural Network Architecture Design, Comparative Performance Analysis

Possible study pathways

One question, different levels

Research master’s

Developing and benchmarking novel interpretable neural network architectures.

Doctoral

Theoretical advancement of intrinsically interpretable-by-design neural network workflows.

originalityAdvanced
methodologyAdvanced
Data accessAccessible
ethicsModerate

Qualification signal

88/100

  • Focuses on the proposed routes of adaptive routing and temporal diagnostics.
  • Open-access scholarly source and DOI metadata verified

Provenance

Research Idea Registry curation

  • DOI and bibliographic metadata independently resolved
  • Open-access status verified
  • The research direction is transparently marked as AI-inferred
The public contributor code contains no name or account email.

APA 7 source

Swamy, V., Frej, J., & Käser, T. (2025). Viewpoint: The Future of Human-Centric Explainable Artificial Intelligence (XAI) is not Post-Hoc Explanations. Journal of Artificial Intelligence Research, 84. https://doi.org/10.1613/jair.1.17970

Paper abstract and discussion context; AI-inferred direction

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