Episode 98: Building Trust in AI Through Model Interpretability
From Value Driven Data Science by Dr Genevieve Hayes
March 18, 2026 · 25 min · Episode 98
About this episode
This episode discusses the importance of model interpretability in AI to build trust among stakeholders in high-stakes environments.
When your machine learning model makes a decision that affects someone's medical treatment, financial security, or legal rights, "the algorithm said so" isn't good enough. Stakeholders need to understand why models make the decisions they do, and in high-stakes environments, model interpretability becomes the difference between AI adoption and AI rejection. In this episode, Serg Masis joins Dr. Genevieve Hayes to share practical strategies for building interpretable machine learning models that earn stakeholder trust and accelerate AI adoption within your organisation. You'll learn: The crucial distinction between interpretable and explainable models [07:06] Why feature engineering matters more than algorithm choice [14:56] How to use models to improve your data quality [17:59] The underrated technique that builds stakeholder trust [21:20] Guest Bio Serg Masis is the Principal AI Scientist at Syngenta, a leading agricultural company with a mission to improve global food security. He is also the author of Interpretable Machine Learning with Python and co-author of the upcoming DIY AI and Building Responsible AI with Python . Links Serg's Website Connect with Serg on LinkedIn…
People in this episode
Host: Dr Genevieve Hayes
Guest: Serg Masis
Topics covered
- AI trust
- model interpretability
- machine learning
- stakeholder engagement
- data quality
Keywords
- model interpretability
- AI adoption
- stakeholder trust
- feature engineering
- data quality
Mentioned in this episode
Organizations: Syngenta
Books & works: Interpretable Machine Learning with Python, DIY AI, Building Responsible AI with Python
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