RAISP02-BP07 Incorporate explainability mechanisms into the core AI system
Adding explainability to your AI system assists to address explainability release criteria by verifying stakeholders can understand and trust how decisions are made for your specific use case. Include confidence scores with predictions to show how certain the model is about its outputs, and for generative AI systems, use techniques such as content attribution, and token probabilities to explain what influenced the generated content. When explanations are critical, use interpretable models like decision trees that are simple to understand and when more complex models are required add explanation tools (like LIME or SHAP) afterward to interpret their decisions.
Level of risk exposed if this best practice is not established: High
Implementation considerations
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Build confidence scoring into your system's output pipeline so users can see how certain your model is about each prediction. Test different confidence calculation methods to find ones that actually correlate with accuracy, since some approaches give misleading confidence scores that don't assist users to make better decisions.
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Choose interpretable model architectures, such as decision trees or linear models when stakeholders need to understand exactly how decisions are made. Compare the performance trade-offs between interpretable and complex models for your specific use case to see if the explanation benefits justify accuracy costs.
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Add explanation tools like LIME or SHAP to complex models that you can't make interpretable but still need to explain. Test these tools with your actual users to make sure the explanations are helpful rather than confusing, since some explanation methods work better for different types of models and use cases.
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For generative AI systems, build in techniques like chain-of-thought prompting that show the reasoning process, content attribution that traces outputs back to source material, and token probability displays that reveal uncertainty. Test these explanation methods to see which ones assist users to understand and trust the generated content. For example, each response from an Amazon Bedrock agent is accompanied by a trace that details the steps being orchestrated by the agent. The trace assists to follow the agent's reasoning process that leads it to the response it gives at that point in the conversation.
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Build automatic source attribution into your Retrieval Augmented Generation (RAG) system by linking retrieved information directly to its origin document with specific citations, page numbers, and document identifiers. Display these citations alongside generated content so users can independently verify where information came from.
Create explanation validation processes that check whether your explainability mechanisms are effective in assisting users make better decisions about trusting or acting on your system's outputs. Regularly test explanations with real users to catch when explanation methods become misleading or stop being useful as your system evolves.
Resources
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