The Ultimate Guide to AI Accuracy: Part 3: Awareness

Last post, we identified critical failure modes. This post investigates processes to address failure modes from the outset of AI development and usage.

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Strategy 2: Building Accuracy Awareness into AI Creation and Adoption

In our exploration of AI accuracy, we previously highlighted the importance of understanding critical failure modes and critical thinking.

Yet, individual awareness alone cannot guarantee AI accuracy. The key lies in weaving accuracy considerations into the very fabric of AI creation and adoption.

This approach sets the stage for responsible AI use throughout its lifecycle, yielding three crucial benefits:

1. Enhanced Performance: By prioritizing accuracy from the outset, we create AI systems that are inherently more precise and reliable.
2. User Trust: AI systems designed with accuracy at their core consistently deliver dependable results, fostering trust and encouraging responsible adoption.
3. Risk Mitigation: By embedding accuracy awareness into our initial AI strategy, we reduce the potential for unintended harm and legal complications.

This strategy proposes two steps: (1) an accuracy and risk assessment (focus of this post) and (2) foundational documentation for model accuracy

Step 1: Assessing Accuracy and Risk of Harm

At the heart of this strategy is a systematic process for evaluating risks and accuracy at the earliest stages of AI development or adoption. This proactive approach identifies and addresses potential issues before they can impact users, laying a strong foundation for reliable AI performance.

This process should be included in a comprehensive AI policy covering AI creation and usage that is part of training and onboarding. It is important to regularly review and update the policy to keep up with new AI and legal developments and to mitigate new known common errors.

For our bankruptcy platform, we might conduct an initial audit of the AI's advice accuracy before creation and launch by following the steps below:

a) Risk Level: Evaluate the potential for inaccuracy and its consequences. Consider simple questions: Where are the model’s known weaknesses and failure modes? Where might inaccuracies result in harm to individuals?

For our bankruptcy platform, we'd be particularly cautious if the AI offers direct legal advice without human oversight or processes sensitive financial data. This classification helps prioritize accuracy efforts where they matter most, ensuring that resources are focused on the areas with the highest potential impact.

b) Baseline and Goals: Establish clear, measurable accuracy goals. Set realistic benchmarks.

For our bankruptcy AI, we might target a 90% agreement rate with expert lawyers' opinions and a 95% success rate for bankruptcy filings using AI-generated advice. We will also create a process to monitor our accuracy rates over time to get ahead of issues.

c) Safeguards: Implement straightforward accuracy safeguards, with more safeguards for higher risk activities.

For our bankruptcy platform, we might consider a tiered approach:

Basic Safeguards for All Cases

• Data Quality: We use reputable financial data sources and perform basic consistency checks on user inputs. Regularly updating the training dataset with recent, verified bankruptcy cases and implementing automated checks for data consistency and completeness in user submissions.
• Human Oversight: We conduct periodic random audits of AI recommendations by a qualified team member and straightforward user feedback system. We've also generated a basic incident response plan for AI failures or data breaches.
• Disclosure & Explainability: We clearly label all advice as AI-generated and provide a brief explanation of how the AI works. We offer simple, jargon-free explanations and support for each recommendation.

Robust Protections for High-Risk Situations

In scenarios where the potential for harm is greatest, we employ our strongest safeguards:

• Data Quality: We implement rigorous validation processes, potentially including third-party verification for critical financial data.
• Human Oversight: We require mandatory human review for cases involving vulnerable populations or high-value assets.
• Disclosure & Explainability: We implement a comprehensive informed consent process, clearly outlining potential risks and limitations. We offer an in-depth explanation of the AI's decision-making process, including an audit trail of key decision points. We might also implement a "fallback" mechanism that defaults to general, conservative advice when the AI's confidence level falls below a certain threshold.

By tailoring our accuracy measures to the level of risk, we create a flexible system that maximizes protection where it's most needed while remaining manageable for smaller organizations or those with limited technical resources. This approach allows us to balance the benefits of AI-driven advice with the necessary safeguards to ensure accuracy and user protection.

Remember, the goal is not perfection, but rather a thoughtful, risk-aware approach to AI accuracy. By implementing these strategies, we can significantly enhance the reliability and trustworthiness of our AI systems, even with limited resources.

Future posts will dive into the Steps 2 and 3 to build Accuracy Awareness into AI Creation and Adoption.

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