An Analogy goes a long way: Using simple analogies & examples to break down AI for your customers.

The excitement around AI has sparked a widespread curiosity about how AI solutions are developed and how models generate their recommendations. However, the technical complexities often make this understanding challenging to grasp. To bridge this gap, I’ve found that using simple use cases or relatable analogies can effectively demystify these concepts and make them more accessible. To that end, I have 5 examples I’d like to share with all of you.

Explaining the accuracy vs interpretability trade-off using a match-making analogy:

Navigating the accuracy vs. interpretability trade-off is a significant challenge for AI/ML teams. Some customers prioritize a model’s accuracy over understanding its logic, while others prefer comprehensible logic even at the expense of accuracy.

Imagine you’re a matchmaker for your friends. Your matchmaking method (akin to an ML model) is based on your observations (data).

Your method may seem illogical for Friend A, but they trust your track record. They don’t fully grasp your reasoning, like focusing on matching social energy levels, but the successful dates encourage them to continue following your advice. Gradually, they begin to understand your approach. This mirrors relying on a highly accurate but less interpretable AI model.

Conversely, Friend B needs to understand and agree with your method. They have beliefs about essential factors that might not align with your observations. You adjust your approach to include their feedback. Initially, the results are mixed, but they grow more comfortable as they understand what works. This journey to refine the strategy and include more complex factors is akin to choosing a more interpretable but initially less accurate model.

Explaining how ML models learn from new data to maintain consistently good results:

Imagine your goal is to make the best-fried rice, but people’s tastes change over time. To keep up, you might collect feedback and tweak the quantities or cooking times while keeping the essential ingredients and methods the same. This is like re-training a model: you make minor, automated adjustments based on new data.

But what if taste changes so much that minor tweaks aren’t enough? Maybe you need to replace chicken with shrimp and tofu or use a different cooking method for a unique flavor. This is like rebuilding a model. You’re changing fundamental aspects, such as introducing new variables or altering the algorithm. Like in cooking, these significant changes in AI solutions require manual input and thoughtful decision-making. Your AI solution will never determine what new variable needs to be introduced or decide to switch up algorithms. For that leap in logic, there has to be a guiding hand.

Explaining what a 95% confidence interval means and how it can be used to describe the outputs of your ML Model:

You start the season strong, hitting 80% of your free throws in the first few games. As the season progresses, your free throw percentage varies from game to game. In some games, you might hit 85%; in others, 75%; occasionally, it might dip to 70%. Based on this data, you calculate a 95% confidence interval for your free throw percentage.

In practical terms, if you were to look at many samples of your performance over different sets of games, 95% of those samples would have an average free throw percentage within 70% to 80%. Therefore in the future, it is safe for coaches & teammates to see you as a 70–80% shooter.

Using our model to predict customer churn, we’ve tracked the results over an extensive dataset. Based on our data, we can confidently tell customers that the model’s accuracy is within a 50–60% range. This means that if you draw multiple random samples from your customer dataset, 95% of those samples will show the model correctly predicting 50–60% of the likely churn cases. Customers can feel confident that the model will maintain a 50–60% accuracy range going forward unless there is a significant shift in the market or underlying data.

AI is more of an approach not a feature:

Think of how Netflix recommends movies to you based on your watch history. The feature is the recommendation engine, machine learning is the underlying logic that selects the best recommendations. Quite frankly if simply looking at basic metrics like genres, run time, popularity of actors, box office $ did the trick, no need for AI. The feature people are looking for is a good recommendation engine, not an AI recommendation engine.

Explaining how K — Means clustering works by using a pickup basketball game as an example:

K-Means clustering is like organizing a pickup basketball game with 20 players into teams of 5. Even though no one has predefined roles (like point guard or center), nor has anyone been on any formal team, you know their skills in shooting, stealing, and blocking. You use these attributes to create the best teams based on fit. Similarly, K-Means clustering uses available data attributes to group items by compatibility in the absence of any previous grouping. It iterates through different groupings, refining them until it finds the optimal clusters, just as you would adjust your teams based on performance.