The Ultimate Guide to AI Accuracy: Part 2: Awareness

Last post, we identified 3 critical failure modes. This post will identify #4 and #5 with an example application of b

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(4) Not following instructions or providing consistent responses

Definition: AI disregards given guidelines or constraints in its responses or is inconsistent in its responses

Example: When instructed to provide a brief summary of a legal case in 100 words, the AI generates a 500-word detailed analysis instead. OR the AI states that the statute of limitations for filing a personal injury claim is two years in one response, but three years in another for the same jurisdiction.

Indicator: Conflicting information provided by the AI when asked similar questions at different times or by different users.

Indicator: The AI's output consistently fails to adhere to specific parameters or instructions provided in the prompt.

(5) Lack of transparency in decision-making

Definition: AI systems operate as "black boxes," making it difficult to understand their reasoning or decision process.

Example: An AI recommends denying parole to an inmate but cannot explain the specific factors or weights used in reaching this decision.

Indicator: Inability to provide clear, step-by-step explanations for how the AI arrived at its conclusions or recommendations.

AI Accuracy: Running Example

Let's apply these steps to our scenario with the bankrupcty filing organization:

Step 1: Educate yourself on common failure modes and adopt healthy skepticism

• Team members familiarize themselves with the failure modes listed above, particularly focusing on hallucination and biased outputs in the context of bankruptcy law.
• They adopt a skeptical approach, always cross-checking AI-generated advice on eligibility criteria for Chapter 7 bankruptcy against current legal standards and precedents.

Step 2: Foster a culture of continuous learning, especially tracking mistakes and issues

• Implement a system to log and categorize any discrepancies found between AI-generated advice and verified legal information.
• Regularly update the team on changes in bankruptcy law and AI advancements in legal tech.

Step 3: Postmortems. Think about how to avoid the mistakes you've found in your own work and in others

• After each major project or quarterly, conduct a thorough review of logged issues.
• Analyze patterns in AI mistakes, such as consistent misinterpretation of certain financial data or overlooked legal nuances in bankruptcy cases.
• Develop strategies to mitigate these issues, such as implementing additional verification steps for specific types of financial information or enhancing the AI's training data with more diverse bankruptcy case examples.

By following these steps, the team can significantly improve the accuracy and reliability of their AI-powered bankruptcy filing assistance, ensuring better outcomes for users navigating complex legal processes.

Future posts will dive into additional approaches to improve awareness, such as risk assessment and transparency, and prompt engineering best practices. Part two and three will address grounding and verification techniques 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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