Board directors need high-level knowledge about AI and machine learning models to effectively evaluate and decide upon AI strategies.   

Therefore, the resourceful and pro-active chairman of my client invited me to run a workshop together with the rest of the board.

It was an efficient way for them to gain some relevant knowledge – just prior to the kick off of their AI strategy process in collaboration with their internal experts.

We covered quite a lot in an afternoon! 

1.   The Basics:

Introduction to the basic types of machine learning models such as supervised learning (e.g., linear regression, decision trees), un-supervised learning (e.g., clustering, dimensionality reduction), and reinforcement learning.

Each type has its own use cases, strengths and weaknesses.

2.   Purpose of the Model:

Understand what a model is designed to achieve.

Is it for prediction (e.g., sales forecasting), classification (e.g., spam detection), clustering (e.g., customer segmentation), or something else? And why it matters?

3.   Performance Metrics:

Query the key performance indicators (KPIs) used to evaluate the model – why this is relevant.

We briefly touched upon: – accuracy, precision, recall, F1 score, ROC AUC for classification models; Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) for regression models; and silhouette score, Davies-Bouldin index for clustering models. I detected some enthusiasm….

4.   Data Requirements:

Different models require different types and different amounts of data.

An initial understanding of these requirements can provide an insight into the suitability of a model for a given task.

5.   Compliance and Ethics:

What are some important aspects to consider to ensure the models comply with relevant regulations and ethical guidelines, especially when dealing with sensitive data.

6.   Model Interpretability:

Some models, like decision trees, are easier to interpret than others, for example neural networks.

Depending on the application, the ability to understand and explain a model’s decisions may be important.

7.   Maintenance and Update Requirements:

Models may need to be retrained or updated as new data becomes available.

Understanding what these different requirements are, can help assess the ongoing resources needed.

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