# Towards explainable decision support using hybrid neural models for logistic terminal automation

arXiv: https://arxiv.org/abs/2509.07577 · HTML: https://mlsanity.com/paper/2509.07577/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Mechanistic Interpretability for AI Safety -- A Review](https://mlsanity.com/paper/2404.14082/) (2024)
- [Explaining Classifiers with Causal Concept Effect (CaCE)](https://mlsanity.com/paper/1907.07165/) (2019)
- [Causal Machine Learning: A Survey and Open Problems](https://mlsanity.com/paper/2206.15475/) (2022)
- [Concept-based Explainable Artificial Intelligence: A Survey](https://mlsanity.com/paper/2312.12936/) (2023)
- [Ethics-Based Auditing to Develop Trustworthy AI](https://mlsanity.com/paper/2105.00002/) (2021)
- [Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems](https://mlsanity.com/paper/2305.02251/) (2023)
- [The role of causality in explainable artificial intelligence](https://mlsanity.com/paper/2309.09901/) (2023)
- [Exploring Explainability: A Definition, a Model, and a Knowledge Catalogue](https://mlsanity.com/paper/2108.03012/) (2021)
- [Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence](https://mlsanity.com/paper/2112.14466/) (2021)
- [Sparse dynamical system identification with simultaneous structural parameters and initial condition estimation](https://mlsanity.com/paper/2204.10472/) (2022)
- [Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning](https://mlsanity.com/paper/2309.14557/) (2023)
- [Assessing the Auditability of AI-integrating Systems: A Framework and Learning Analytics Case Study](https://mlsanity.com/paper/2411.08906/) (2024)
- [A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization](https://mlsanity.com/paper/2504.03692/) (2025)
- [Digital Twins for Logistics and Supply Chain Systems: Literature Review, Conceptual Framework, Research Potential, and Practical Challenges](https://mlsanity.com/paper/2311.17317/) (2023)

## Abstract (arXiv)

The integration of Deep Learning (DL) in System Dynamics (SD) modeling for transportation logistics offers significant advantages in scalability and predictive accuracy. However, these gains are often offset by the loss of explainability and causal reliability - key requirements in critical decision-making systems. This paper presents a novel framework for interpretable-by-design neural system dynamics modeling that synergizes DL with techniques from Concept-Based Interpretability, Mechanistic Interpretability, and Causal Machine Learning. The proposed hybrid approach enables the construction of neural network models that operate on semantically meaningful and actionable variables, while retaining the causal grounding and transparency typical of traditional SD models. The framework is conceived to be applied to real-world case-studies from the EU-funded project AutoMoTIF, focusing on data-driven decision support, automation, and optimization of multimodal logistic terminals. We aim at showing how neuro-symbolic methods can bridge the gap between black-box predictive models and the need for critical decision support in complex dynamical environments within cyber-physical systems enabled by the industrial Internet-of-Things.