What are the best practices for making an AI system interpretable and transparent?
Artificial intelligence (AI) systems are becoming more powerful and pervasive, but they also pose challenges for trust, accountability, and ethics. How can you ensure that your AI system is not only effective and efficient, but also understandable and transparent? In this article, you will learn some of the best practices for making an AI system interpretable and transparent, and why they matter for your users, stakeholders, and society.
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Clear feature selection:Opt for models like decision trees that are easier for users to understand. Use explanation techniques such as LIME or SHAP to clarify predictions and document the decision-making process thoroughly.
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Publish a clear schema:A detailed schema showing how features affect outcomes demystifies the AI’s decision-making. This transparency aids user trust and meets regulatory standards for accountability.