How can you make your machine learning models interpretable to stakeholders?

Understanding machine learning models ensures that decision-making is transparent, which fosters trust. Making informed decisions requires an understanding of the elements influencing decisions. Adopting models that stakeholders understand is more likely to occur, particularly in regulated industries where disclosures are required, as in the case of the EU's GDPR. Furthermore, interpretability aids in detecting and eliminating biases, avoiding the maintenance of preexisting disparities.

Making machine learning models interpretable to stakeholders is crucial for risk management and decision-making. Start by using models that are inherently interpretable, such as decision trees or linear regression, when possible. For complex models, apply techniques like SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations) to provide insights into model behavior. Visualizations, such as feature importance graphs and partial dependence plots, can enhance understanding. Regularly communicate the rationale behind model decisions in clear, non-technical language to build trust and ensure compliance with ethical and regulatory standards. This approach fosters stakeholder confidence.

To make machine learning models interpretable to stakeholders: Simplify Models: Use simpler, transparent models like decision trees or linear regression. Visualization: Employ visual aids such as graphs, charts, and decision plots to explain model outputs. Feature Importance: Highlight and explain the most influential features in the model's decisions. Example-based Explanations: Provide real-world examples and case studies showing how the model works. Clear Communication: Use non-technical language to explain the model's purpose, function, and results. Tools: Utilize interpretability tools like LIME or SHAP to break down complex models. These strategies help stakeholders understand and trust the model's outcomes.

Regardless of model complexity, describing each model characteristics can be helpful when talking about the model : 1. For data part of the model(model input/output), just to mention which data used for modelling purposes and show what is end result of the model. 3. For model calculation engine, use geometric objects and decision trees and highlight top 3-5 strong variable have huge impact on model result. 4. For model performance metrics [variable significance and predictive power] try to use simple sentences like i.e. if R^2=50%, it can be worded like " 50% of model output is explained by model inputs so, we see half of the picture and probably other half will be seen if we use other factors".