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# Exploring a Machine Learning Model with Interactive Visualization and a Chat Assistant for Bread-Making Insights

Omi Johnson, Briggs Twitchell, Mélanie Munch, Kamal Kansou, C Baudrit, Georgios Katsirelos, Anastasia Bezerianos, Nadia Boukhelifa

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Omi Johnson, Briggs Twitchell, Mélanie Munch, Kamal Kansou, C Baudrit, et al.. Exploring a Machine Learning Model with Interactive Visualization and a Chat Assistant for Bread-Making Insights. GreenFoodTech 2025, Apr 2025, Saint Malo (Palais du Grand Large), France. hal-05285797

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https://hal.inrae.fr/hal-05285797v1

Submitted on 26 Sep 2025

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Open licence - etalab
Exploring a Machine Learning Model with Interactive Visualization
and a Chat Assistant for Bread-Making Insights

OMI JOHNSON, Khoury College of Computer Sciences, Northeastern University, USA

BRIGGS TWITCHELL, Khoury College of Computer Sciences, Northeastern University, USA

MÉLANIE MÜNCH, INRAE, UMR STLO, France

KAMAL KANSOU, INRAE, Biopolymères Interactions et Assemblages UR1268 -

BIA, CÉDRIC BAUDRIT, INRAE, Université de Bordeaux, I2M, France

GEORGE KATSIRELOS, UMR MIA-Paris, INRAe, Université Paris-Saclay, France

ANASTASIA BEZERIANOS, Université Paris-Saclay, INRIA, CNRS, France NADIA

BOUKHELIFA, UMR MIA-Paris, INRAe, Université Paris-Saclay, ISC-PIF, France

**Keywords:** machine learning, visualization, expert knowledge, bread-making, bayesian networks

Modelling complex systems from data using Machine Learning (ML) can improve understanding and support decision-making. However, these models' outputs are often not intuitive, especially for users without a statistical background.

In this work, we explore different methods to help domain experts without statistical expertise better understand complex ML models in food processing. Our case study focuses on a Bayesian Network (BN) model that simulates bread dough behaviour, reflecting the bread-making process [1].

To enable domain experts to understand and validate this BN model, we investigate two approaches that combine ML with interactive interfaces. Following a user-centred design methodology [2], we collaborated with ML modellers and industry experts in French bread-making to develop two interactive applications for exploring the BN model.

The first application leverages intuitive visual representations [3], focusing on interactive evidence propagation, customisable views, and exposed data as key features to aid understanding. The tool was designed and validated with domain experts and BN specialists and proven effective to explore and validate the model. We share lessons learned on designing visualizations that make complex models accessible and foster dialogue between modellers and domain experts.

The second application examines how advances in Large Language Models (LLMs) might help explain ML model outputs. This platform integrates ChatGPT-4 to assist in explaining model operations, such as variable dependencies and causal relationships. Qualitative feedback sessions with modellers and a domain expert revealed some unique advantages but also notable challenges in using current LLMs for model explainability.

Future work will aim to refine these tools, and investigate the value of combining ML, interactive visualizations, and domain expertise to co-construct effective ML models for food processing, especially in cases requiring domain-specific insights. This approach underscores the importance of participatory and user-centred design in developing ML tools that non-specialist users can understand, validate, and ultimately trust.
## References

[1] Mélanie Munch, et al. Diagnosis based on sensory data: Application to wheat grading quality. Innovative Food Science & Emerging Technologies 96 (2024), 103771.

[2] D, Norman. (2013). The design of everyday things: Revised and expanded edition. Basic books.

[3] S. K. Card et al. (Eds.). Readings in information visualization: using vision to think, (1999), Morgan Kaufmann.

## Acknowledgements

This research was supported by DATAIA (ANR-17-CONV-0003). The authors thank the ANR EVAGRAIN project (ANR-20-CE21-0008) partners and Pierre-Henri Wuillemin for his assistance with pyAgrum and valuable feedback.