What are the most effective ways to explain AI models?

1 Visualize the model

One of the simplest and most intuitive ways to explain AI models is to use visualizations that show how the model works, what inputs and outputs it uses, and how it makes decisions. For example, you can use graphs, charts, diagrams, or heat maps to illustrate the model structure, performance, features, or predictions. Visualizations can help you highlight the key aspects of your model, such as accuracy, bias, uncertainty, or trade-offs.

2 Use natural language

Another way to explain AI models is to use natural language that describes the model in terms of concepts, rules, or scenarios that are familiar and relevant to your audience. For example, you can use plain English to explain the model's goals, criteria, or outcomes, or use metaphors, analogies, or examples to relate the model to real-world situations. Natural language can help you bridge the gap between the technical and the human aspects of your model, and make it more understandable and trustworthy.

4 Compare the model

A fourth way to explain AI models is to compare the model with other models, methods, or alternatives that your audience might be familiar with or interested in. For example, you can use tables, charts, or metrics to show how the model performs relative to other models, or how it differs from other approaches or solutions. Comparison can help you justify the model's choices, advantages, or disadvantages, and address any questions or concerns your audience might have.

5 Explain the model's impact

A fifth way to explain AI models is to explain the model's impact on your audience, your organization, or your domain. For example, you can use stories, cases, or testimonials to show how the model affects or benefits your audience's goals, needs, or values, or how it contributes to your organization's mission, vision, or strategy. Impact can help you convey the model's value, relevance, or implications, and motivate your audience to engage with or support the model.

6 Follow best practices

A sixth way to explain AI models is to follow best practices and standards that guide the development, deployment, and evaluation of AI models. For example, you can use frameworks, principles, or guidelines that outline the ethical, legal, or social aspects of AI models, or use methods, tools, or metrics that ensure the quality, reliability, or robustness of AI models. Best practices can help you adhere to the norms, expectations, or requirements of your field, industry, or society, and enhance the credibility, accountability, or transparency of your model.