How can you generate and present explanations?

1 What are explanations?

Explanations are statements or representations that provide information about the reasons, processes, or mechanisms behind an AI system's behavior or output. They can help you and others to understand, evaluate, and improve your AI system, as well as to comply with ethical and legal standards. Explanations can vary in their level of detail, complexity, and format, depending on the purpose, audience, and context of the explanation.

2 Why are explanations important?

Explanations are important for several reasons. First, they can increase the trust and confidence in your AI system, by showing that it is reliable, consistent, and fair. Second, they can enhance the performance and quality of your AI system, by revealing its strengths, weaknesses, and areas for improvement. Third, they can facilitate the collaboration and communication between you and other stakeholders, by enabling feedback, dialogue, and accountability. Fourth, they can support the compliance and governance of your AI system, by demonstrating that it meets the ethical and legal requirements and standards.

3 How can you generate explanations?

When generating explanations for your AI system, you have a variety of methods to choose from depending on the type, complexity, and design of the system. Interpretability refers to understanding the internal workings and logic of your AI system, such as its features, parameters, and algorithms. You can use feature selection, regularization, dimensionality reduction, or visualization to increase interpretability. Transparency involves accessing and inspecting the data, code, and documentation of your AI system. You can use data provenance, code review, or documentation standards to ensure transparency. Counterfactuals are hypothetical scenarios that show how the output of your AI system would change if some input or condition were different. They can help you identify biases, errors, or anomalies by understanding the causal relationships between input and output. You can generate counterfactuals with perturbation, optimization, or simulation.

4 How can you present explanations?

When presenting explanations for your AI system, there are a few aspects to consider. The format should be tailored to the level of detail, complexity, and clarity you wish to convey, as well as the expectations and preferences of your audience. The content should be relevant, accurate, and comprehensive for the purpose of the explanation and the interests of your audience. Additionally, the style should be adapted to ensure credibility, persuasiveness, and accessibility, as well as be appropriate for the background and knowledge of your audience. Generating and presenting explanations for your AI system is not only a technical challenge but also a social and ethical one. It is important to consider the implications and consequences of your explanations, as well as the values and rights of your stakeholders in order to create more responsible, trustworthy, and beneficial AI systems.