When my machine learning model’s decisions are under scrutiny, I focus on transparency and clarity. - I explain why I chose a specific model based on factors like accuracy, interpretability, and data complexity. - For transparency, I use feature importance and visualizations to show how different factors influence the output. - If the model is complex (like a neural network), I use explainability tools (e.g., SHAP, LIME) to break down decisions. - I also compare it with simpler models to show the trade-offs made. Most importantly, I ensure that the model’s choices align with the business context and ethical considerations for full accountability.

This is one of the specificities of Machine Learning, as it is essential for the business to identify with the model. In other words, to convince the business, the model must be interpretable, meaning it should reflect a real-world reality. However, this cannot be the only criterion, as in my opinion, robustness should be the priority.

I'd justify my ML model selection by: - Explaining why the model architecture matches our specific problem and data characteristics - Presenting key performance metrics across diverse test sets, including challenging edge cases - Showing benchmarks against alternatives with clear rationale for our choice - Addressing fairness by demonstrating consistent performance across demographic groups - Outlining our monitoring framework that tracks performance and triggers retraining when needed

Here’s how I do it: - Explain in layman terms: Avoid technical jargon and describe the model’s logic in a way that non-technical people can understand. - Use EDA: Show how the data looks, what trends it reveals, and why those patterns matter for the model’s decisions. - Apply Explainable AI methods: Use techniques like SHAP values, LIME, and feature importance to show which factors influence predictions. - Keep communication open: I encourage questions and discussions to ensure everyone understands and trusts the model’s decisions. The key is to make the model’s reasoning transparent and easy to follow so that people feel confident in its results.