Delivering model insights

Model metric performance, when used exclusively for model comparisons and the model choosing process, is often not the most effective way to reliably obtain the true best model. When people care about the decisions that can potentially be made by the machine learning model, they typically require more information and insights to eventually put their trust in the ability of the model to make decisions. Ultimately, when models are not trusted, they don’t get deployed. However, trust doesn’t just depend on insights of the model. Building trust in a model involves ensuring accurate, reliable, and unbiased predictions that align with domain expertise and business objectives, while providing stakeholders with insights into the model’s performance metrics, decision-making logic, and rationale behind its predictions. Addressing potential biases and demonstrating fairness are crucial for gaining confidence in the model’s dependability. This ongoing trust-building process extends beyond initial deployment, as the model must consistently exhibit sound decision-making, justify predictions, and maintain unbiased performance. By fostering trust, the model becomes a valuable and reliable tool for real-world applications, leading to increased adoption and utilization across various domains and industries.

Deliver model insights that matter to the business. Other than delivering model insights with the obvious goal of ensuring model trust and eliminating trust issues, actual performance metrics are equally important. Make sure you translate model metrics into layman’s business metrics whenever possible to effectively communicate the potential positive impact that the model can bring to the business. Success metrics, which are defined earlier in the planning phase, should be reported with actual values at this stage.

The process of inducing trust in a model doesn’t stop after the model gets deployed. Similar to how humans are required to explain their decisions in life, machine learning models (if expected to replace humans to automate the decisioning process) are also required to do so. This process is called prediction explanations. In some cases, model decisions are expected to be used as a reference where there is a human in the loop that acts as a domain expert to verify decisions before the decisions are carried out. Prediction explanations are almost always a necessity in these conditions where the users of the model are interested in why the model made its predictions instead of using the predictions directly.

Model insights also allow you to improve a model’s performance. Remember that the machine learning life cycle is naturally an iterative process. Some concrete examples of where this condition could happen are as follows:

• You realize that the model is biased against a particular group and either go back to the data acquisition stage to acquire more data from the less represented group or change to a modeling technique that is robust to bias
• You realize that the model performs badly in one class and go back to the model development stage to use a different deep learning model loss function that can focus on the badly performing class

Deep learning models are known to be a black box model. However, in reality, today, there have been many published research papers on deep learning explanation methods that have allowed deep learning to break the boundaries of a black box. We will dive into the different ways we can interpret and provide insights for deep learning models in Part 2 of this book.

Now that we have more context of the processes involved in the deep learning life cycle, in the next section, we will discuss risks that can exist throughout the life cycle of the project that you need to worry about.