Automated Machine Learning Explainability Pipeline

Ensure your model consistently provides the necessary insights for compliance and validation.

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With the recent surge in the AI buzzword, many companies have made a concerted effort to implement AI products within their businesses, including a more conscious effort to adopt specific solutions with Machine Learning (ML). According to the 2025 DemandSage report, 48% of companies worldwide have adopted ML in their business functions, and the ML market is estimated to grow by 36.08%.

As the adoption of ML increases, situations will certainly arise in which it may cause harm. For instance, a 2025 ScienceDirect review by Mohammed and Malhotra revealed multiple failures in ML systems in healthcare, leading to misdiagnoses due to the lack of clear reasoning behind the model’s decisions.

The reasoning behind ML decisions becomes increasingly critical as we begin to delegate decisions to ML algorithms. This is where the concept of explainability in ML comes into play. By building our ML model with explainability considerations that support the model output, we can ensure compliance and build trust.

In this article, we will explore how to extend our ML model with explainability while automating the report creation to ensure that our…