# MATCH: Engineering Transparent and Controllable Conversational XAI Systems through Composable Building Blocks

arXiv: https://arxiv.org/abs/2511.22420 · HTML: https://mlsanity.com/paper/2511.22420/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [ViperGPT: Visual Inference via Python Execution for Reasoning](https://mlsanity.com/paper/2303.08128/) (2023)
- [Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models](https://mlsanity.com/paper/2304.09842/) (2023)
- [The What-If Tool: Interactive Probing of Machine Learning Models](https://mlsanity.com/paper/1907.04135/) (2019)
- [TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations](https://mlsanity.com/paper/2207.04154/) (2022)
- [From Understanding to Utilization: A Survey on Explainability for Large Language Models](https://mlsanity.com/paper/2401.12874/) (2024)
- [Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing](https://mlsanity.com/paper/2210.15889/) (2022)
- [Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant](https://mlsanity.com/paper/2501.17546/) (2025)
- [XAI for All: Can Large Language Models Simplify Explainable AI?](https://mlsanity.com/paper/2401.13110/) (2024)
- [Tell Me a Story! Narrative-Driven XAI with Large Language Models](https://mlsanity.com/paper/2309.17057/) (2023)
- [XAIR: A Framework of Explainable AI in Augmented Reality](https://mlsanity.com/paper/2303.16292/) (2023)
- [From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent](https://mlsanity.com/paper/2209.02552/) (2022)
- [TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization](https://mlsanity.com/paper/2209.09227/) (2022)
- [What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations](https://mlsanity.com/paper/2002.05674/) (2020)
- [AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making](https://mlsanity.com/paper/2411.10490/) (2024)
- [Composable Building Blocks for Controllable and Transparent Interactive AI Systems](https://mlsanity.com/paper/2506.02262/) (2025)

## Abstract (arXiv)

While the increased integration of AI technologies into interactive systems enables them to solve an increasing number of tasks, the black-box problem of AI models continues to spread throughout the interactive system as a whole. Explainable AI (XAI) techniques can make AI models more accessible by employing post-hoc methods or transitioning to inherently interpretable models. While this makes individual AI models clearer, the overarching system architecture remains opaque. This challenge not only pertains to standard XAI techniques but also to human examination and conversational XAI approaches that need access to model internals to interpret them correctly and completely. To this end, we propose conceptually representing such interactive systems as sequences of structural building blocks. These include the AI models themselves, as well as control mechanisms grounded in literature. The structural building blocks can then be explained through complementary explanatory building blocks, such as established XAI techniques like LIME and SHAP. The flow and APIs of the structural building blocks form an unambiguous overview of the underlying system, serving as a communication basis for both human and automated agents, thus aligning human and machine interpretability of the embedded AI models. In this paper, we present our flow-based approach and a selection of building blocks as MATCH: a framework for engineering Multi-Agent Transparent and Controllable Human-centered systems. This research contributes to the field of (conversational) XAI by facilitating the integration of interpretability into existing interactive systems.