The Ultimate Guide to AI Accuracy: Part 4: Awareness

Last post, we identified a process to assess and mitigate accuracy risks and harm, and how that could be part of your AI policy. This post elaborates on documentation that promotes AI accuracy.

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Step 2: Foundational Documentation for Accuracy

Creating clear records of AI models from the outset is crucial for maintaining and enhancing accuracy. This step builds on our accuracy awareness by providing a comprehensive picture of the AI's capabilities and limitations from day one. It facilitates informed decision-making about model updates and enables better scrutiny throughout the AI's lifecycle.

For our bankruptcy advice AI, this documentation serves as a roadmap, allowing us to track improvements and identify areas needing attention. It's a valuable resource for both developers and users, promoting responsible and accurate use of the AI system.

To implement this step:

a) Documentation: Create comprehensive "model cards" for each AI component before deployment. For our bankruptcy AI, this would include a detailed description of the model architecture (e.g., "GPT-3 fine-tuned on bankruptcy case law"), training data sources and preprocessing steps (e.g., "1 million anonymized bankruptcy filings from 2010-2023"), performance metrics on test sets (e.g., "95% accuracy in debt classification on 10,000 held-out cases"), and known limitations (e.g., "May struggle with complex international asset structures"). This documentation serves as a crucial accuracy baseline, allowing us to track improvements and identify areas needing attention.

b) Accessibility: Make initial documentation accessible to both technical teams and relevant stakeholders. This could involve creating a secure online repository with role-based access for developers, legal experts, and management, developing simplified versions for non-technical stakeholders (focusing on capabilities and limitations), and scheduling regular cross-functional meetings to review and discuss the documentation. This promotes transparency and allows for multidisciplinary input on accuracy improvements from the start, ensuring that diverse perspectives contribute to the AI's development and refinement.

c) Updates: Establish a process for regularly updating the documentation to reflect changes in the AI model. This might include implementing a version control system for the documentation (linked to model updates), assigning a dedicated team member to maintain and update the documentation, sending team-wide emails when significant changes are made, and setting up automated alerts for when key performance metrics change significantly. By keeping the documentation current, we ensure that accuracy considerations remain at the forefront of ongoing development and deployment efforts.

For our bankruptcy advice AI, this upfront documentation allows us to track accuracy improvements over time and provides a clear picture of the AI's capabilities and limitations from day one. It serves as a valuable resource for both developers and users, promoting responsible and accurate use of the AI system.

By implementing these steps - initial accuracy and risk assessment and foundational documentation - we can significantly enhance the accuracy of our AI systems from the outset.

This strategy-oriented approach builds awareness of accuracy risks into the very foundation of AI creation and adoption, leading to more reliable, trustworthy, and effective AI applications from their inception.

Future posts will dive into Strategies 2 and 3, grounding and verification respectively.

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Stay tuned to discover how you can transform your internal processes to scale faster and better, becoming a trusted strategic advisor

I'd be curious to hear if you've experienced similar operational challenges. If so feel free to share in the comments or reach out to me directly.

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