How can you work with explainable AI?

1 What is explainable AI?

Explainable AI is the ability of AI systems to provide explanations for their outputs, such as predictions, recommendations, or actions. These explanations can help users, developers, regulators, and other stakeholders to understand how the AI system works, what factors influence its decisions, and how reliable and fair it is. Explainable AI can also help to improve the performance, accuracy, and robustness of AI systems by identifying errors, biases, or anomalies.

2 Why is explainable AI important?

Explainable AI is important for several reasons. First, it can increase the trust and confidence of users and customers in AI systems, especially in high-stakes domains such as healthcare, finance, or security. Second, it can enable the ethical and responsible use of AI, by ensuring that the AI system respects human values, norms, and laws. Third, it can foster the collaboration and communication between humans and AI, by facilitating the feedback, learning, and adaptation of both parties.

3 How can you work with explainable AI?

There are various methods and techniques to work with explainable AI, depending on the type and complexity of the AI system, the level and format of the explanation, and the target audience. For example, interpretability involves the inherent transparency and simplicity of the AI system, such as using linear models, decision trees, or rule-based systems that can be easily understood by humans. Post-hoc explanation refers to the generation of explanations after the AI system produces its output. This could involve visualizations, saliency maps, feature importance, or counterfactuals to highlight the relevant inputs, outputs, or alternatives. Additionally, interactive explanation involves providing explanations through a dialogue or query system where users can ask questions and receive appropriate answers or clarifications from the AI system.

4 What are the challenges of explainable AI?

Working with explainable AI is not without challenges. These include a trade-off between the accuracy and explainability of the AI system, as well as a lack of clear criteria to evaluate the quality and effectiveness of the explanations. Additionally, there may be difficulty scaling up the explanations to deal with large and diverse data sets, multiple and dynamic tasks, or heterogeneous and distributed AI systems.

5 What are the best practices of explainable AI?

To work with explainable AI successfully, you should define the purpose and scope of the explanation, use multiple and complementary methods, involve the stakeholders, and keep learning and improving. Clarifying why, what, how, and for whom you need the explanation is essential to tailor it accordingly. Combining different methods and techniques can provide a comprehensive and holistic explanation. Involving users, developers, regulators, and other stakeholders in the design, development, testing, and evaluation of the explanations can help you solicit their feedback and input. Additionally, you should monitor and update the explanations as the AI system evolves or changes to incorporate new data, knowledge, or insights.

6 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?