Explaining Machine Learning Models to Non-Technical Stakeholders

As machine learning (ML) models become more prevalent in various industries across the globe, one of the crucial factors that comes into play is communicating the insights derived from these models effectively. ML models generate predictions based on data inputs and use intricate algorithms to learn patterns and relationships within the data. However, the details of the algorithmic inner workings of these models could be hard to grasp for the general population who aren't intimately familiar with machine learning principles.

Enter the art of machine learning model explanation.

Model explanation refers to the process of conveying to end-users how an ML model arrived at its prediction, thereby building trust and ensuring accountability. Non-technical stakeholders, who ultimately make critical decisions based on the insights provided by these models, need a clear and concise understanding of how the models work without getting overwhelmed by technical jargon.

In this article, we'll explore techniques for explaining machine learning models to non-technical stakeholders in simple terms. We will first delve into the challenges that organizations face when explaining machine learning models to non-technical stakeholders. Then, we will highlight some of the key aspects of machine learning model explanation that should be considered when communicating these insights.

Challenges Faced in Explaining Machine Learning Models to Non-Technical Stakeholders

One of the primary challenges when explaining machine learning models to non-technical stakeholders is the differing level of familiarity with the underlying concepts. In many cases, people who are not well-versed in statistical concepts or machine learning might find it difficult to interpret the results accurately, even if the outputs are presented in a clear manner.

Another challenge is that non-technical stakeholders might not fully comprehend the nuances of the models. They could easily misinterpret the outputs leading to faulty decisions or inadequate deployment of the models.

Finally, machine learning model architectures are often complex and abstract. Explaining these models in a beginner-friendly format that still conveys the necessary details is not trivial.

Essential Components of Explaining Machine Learning Models to Non-Technical Stakeholders

When explaining machine learning models to non-technical stakeholders, there are some essential components that must be considered to ensure the explanations are beneficial and informative. Here are some of these components that are critical in effectively communicating insights from machine learning models.

Start with the basics.

It's essential to start the explanation by establishing a foundation for understanding the basics of machine learning models. While skipping over this could seem like a time-saving technique, we run the risk of assuming the stakeholder has already accumulated some background knowledge in this domain.

Introducing key terms such as "algorithm," "training data," "features," and "labels" provides the necessary context needed to understand how an ML model works. Suppose your audience is already knowledgeable about these terms, then at the very least, ensure you're on the same page as them before proceeding to the explanation of how the model works.

Use visual aids.

Visual aids such as graphs, charts, and diagrams go a long way towards explaining machine learning models to non-technical stakeholders. With the aid of various visualization tools, we can present data in a manner that's understandable to the end-users.

For instance, visualizing the distribution of the input data, the contextual boundaries of the model, and the confidence level of the model's output provides a better understanding of how the model arrived at its predictions.

Empathize with your audience.

In explaining machine learning models to non-technical stakeholders, it is crucial to empathize with your audience. Empathizing boosts your ability to communicate the essence of the model while taking into account the viewpoints and backgrounds of the stakeholders. By understanding what the end-user is trying to achieve or what they hope to gain from the model, we can tailor the explanations to provide useful and relevant insights to them.

Simplify your language.

One of the most significant challenges when explaining machine learning models to non-technical stakeholders is avoiding technical jargon. Technical jargon is a deal-breaker when communicating with non-technical audiences. Using basic explanations free of technical jargon ensures that the content is understandable to most stakeholders.

If there are technical terms that must be used, be sure to explain what they mean in a way that everyone can understand. Avoid acronyms or slang terms that the stakeholders might not be familiar with.

Focus on the main points.

Another key aspect of explaining machine learning models to non-technical stakeholders is to focus on the main points. Rather than going into the details of the inner workings of the model, highlight the most crucial aspects of the results. This way, stakeholders can quickly grasp the essence of the insights and take the necessary steps to leverage the model's outputs to drive decision-making.

Provide examples.

Using relatable examples can make a significant difference when explaining machine learning models to non-technical stakeholders. Examples can illustrate how the model's predictions apply to specific scenarios, which helps stakeholders gain a better understanding of the model's applicability.


Explaining machine learning models to non-technical stakeholders could be daunting. Still, by adopting a concise and effective approach, organizations can bridge the knowledge gap between machine learning and non-technical stakeholders.

Covering the basics, using visual aids, empathizing with your audience, simplifying the language, focusing on the main points, and providing relatable examples are essential components that should be considered when communicating machine learning model output effectively. Following these recommendations will increase the likelihood of successful adoption of the models, unsnarling potential misunderstandings or miscommunications, and lowering the barrier of entry for these models across various industries.

With the continued growth in the development and use of machine learning models and the resulting large amounts of data that they generate, the significance of effective communication is only set to soar. The demand for explainable AI is presently at an all-time high, and we expect it to remain so for years to come. As data analytics and machine learning form an increasingly prevalent part of how businesses and society work, ensuring communication channels are as smooth and frictionless as possible for all stakeholders becomes increasingly paramount to the success of organizations in their respective industries.

Are you curious about what else we have to say at Explainable AI? Head on over to our website, where we regularly showcase the latest insights and techniques related to explaining machine learning models and complex distributed systems. With Explainable AI at the forefront of the industry, you'll always have quick and simple access to the most practical solutions and best practices for bringing data analytics and machine learning to your organisation!

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