How do you explain machine learning results to different audiences?

1 Know your audience

The first step to explain ML results is to know your audience. You need to understand their level of technical knowledge, their interests and needs, and their expectations and questions. For example, a technical audience might want to know the details of the ML model, such as the architecture, the parameters, the performance metrics, and the code. A business audience might want to know the value and impact of the ML results, such as the return on investment, the customer satisfaction, and the competitive advantage. A general audience might want to know the basics and the implications of the ML results, such as the problem statement, the data sources, the main findings, and the ethical and social aspects.

2 Choose the right format

The second step to explain ML results is to choose the right format. You need to select the appropriate medium, style, and tone for your communication. For example, a technical audience might prefer a written report, a presentation, or a demo that shows the ML results in a clear, concise, and accurate way. A business audience might prefer a dashboard, a infographic, or a story that shows the ML results in a relevant, engaging, and persuasive way. A general audience might prefer a video, a podcast, or a blog post that shows the ML results in a simple, accessible, and relatable way.

3 Use visual aids

The third step to explain ML results is to use visual aids. You need to use graphs, charts, tables, images, or animations to illustrate the ML results in a more effective and appealing way. Visual aids can help you to highlight the key points, show the trends and patterns, compare the outcomes, and demonstrate the examples. For example, a technical audience might appreciate a confusion matrix, a ROC curve, or a feature importance plot that shows the ML results in a quantitative and analytical way. A business audience might appreciate a pie chart, a bar chart, or a heatmap that shows the ML results in a qualitative and comparative way. A general audience might appreciate a word cloud, a scatter plot, or a face detection image that shows the ML results in a descriptive and intuitive way.

4 Explain the concepts

The fourth step to explain ML results is to explain the concepts. You need to use plain language, analogies, examples, or metaphors to explain the ML concepts and terms in a more understandable and memorable way. Explain the concepts can help you to avoid jargon, confusion, or misunderstanding. For example, a technical audience might already know the concepts of supervised learning, unsupervised learning, or reinforcement learning, but you might need to explain the specific algorithms, such as k-means clustering, decision trees, or neural networks. A business audience might not know the concepts of ML, but you might need to explain the benefits, challenges, or risks of using ML, such as accuracy, efficiency, or bias. A general audience might not know the concepts of ML, but you might need to explain the applications, limitations, or implications of ML, such as personalization, automation, or privacy.

5 Provide evidence

The fifth step to explain ML results is to provide evidence. You need to use data, statistics, references, or testimonials to support your ML results in a more credible and trustworthy way. Provide evidence can help you to justify your claims, show your sources, and answer your questions. For example, a technical audience might want to see the data, the code, the validation, or the documentation that proves your ML results in a rigorous and reproducible way. A business audience might want to see the numbers, the metrics, the benchmarks, or the feedback that proves your ML results in a measurable and actionable way. A general audience might want to see the facts, the stories, the examples, or the opinions that proves your ML results in a factual and relatable way.

6 Invite feedback

The sixth step to explain ML results is to invite feedback. You need to ask questions, listen to comments, address concerns, or suggest improvements to engage your audience in a more interactive and collaborative way. Invite feedback can help you to check your understanding, clarify your doubts, and improve your communication. For example, a technical audience might have questions about the details, the assumptions, or the alternatives of your ML results, and you might need to answer them with evidence, logic, or reasoning. A business audience might have comments about the value, the impact, or the implementation of your ML results, and you might need to respond them with benefits, solutions, or recommendations. A general audience might have concerns about the ethics, the fairness, or the transparency of your ML results, and you might need to address them with respect, empathy, or awareness.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?