Over 70% of AI systems fail to meet their intended goals due to a lack of proper auditing
A recent study has highlighted the importance of AI auditing in ensuring the reliability and transparency of AI systems. Here's the catch: current methods of auditing AI agents are often inadequate, relying on logging and probabilistic systems that cannot provide certain judgment. This is where AI auditing comes in, and why it's crucial to use causal AI instead of another Large Language Model (LLM).
By reading this article, you'll learn how to improve your AI auditing process using causal AI, and how this approach can increase the transparency and reliability of your AI systems.
What is AI Auditing and Why is it Important?
A recent survey found that 60% of companies are using AI in their operations, but only 20% have a clear understanding of how their AI systems work. This lack of understanding can lead to errors, biases, and unpredictability in AI decision-making.
AI auditing is the process of evaluating and improving the performance of AI systems, ensuring they meet their intended goals and are transparent, explainable, and fair. That said, current methods of auditing AI agents often rely on logging and probabilistic systems, which can be inadequate.
- Logging limitations: Logs can only record what happened, not what was supposed to happen, making it difficult to measure deviation and evaluate performance.
- Probabilistic systems: Using another LLM to evaluate an AI agent's performance can be uncertain and unreliable, as the evaluator itself is a probabilistic system.
- Causal AI: Causal AI can provide a more reliable and transparent approach to AI auditing, by separating the question into two parts that can be answered deterministically: what was the agent supposed to do, and what did it actually do.
How Causal AI Can Improve AI Auditing
Causal AI can improve AI auditing by providing a more transparent and reliable approach to evaluating AI systems. By using causal AI, you can separate the question into two parts that can be answered deterministically: what was the agent supposed to do, and what did it actually do.
This approach can be implemented using the CIEU model — Causal Intent-Execution Unit. Every monitored action produces a five-tuple, which can be used to evaluate the performance of the AI agent and identify areas for improvement.
- CIEU model: The CIEU model provides a deterministic approach to evaluating AI systems, by separating the question into two parts that can be answered deterministically.
- Five-tuple: The five-tuple provides a comprehensive overview of the AI agent's performance, including what was supposed to happen, what actually happened, and how far the agent deviated from its intended goals.
- Transparent evaluation: The CIEU model provides a transparent and reliable approach to evaluating AI systems, by using deterministic comparisons rather than probabilistic evaluations.
Benefits of Causal AI in AI Auditing
The use of causal AI in AI auditing can provide several benefits, including improved transparency, reliability, and explainability. By using causal AI, you can ensure that your AI systems are meeting their intended goals and are transparent, explainable, and fair.
A recent study found that 80% of companies that used causal AI in their AI auditing process reported an improvement in the transparency and reliability of their AI systems.
- Improved transparency: Causal AI can provide a more transparent approach to evaluating AI systems, by using deterministic comparisons rather than probabilistic evaluations.
- Increased reliability: Causal AI can improve the reliability of AI systems, by ensuring that they meet their intended goals and are transparent, explainable, and fair.
- Explainability: Causal AI can provide a more explainable approach to evaluating AI systems, by using deterministic comparisons rather than probabilistic evaluations.
Implementing Causal AI in AI Auditing
Implementing causal AI in AI auditing can be done using the CIEU model. This involves separating the question into two parts that can be answered deterministically: what was the agent supposed to do, and what did it actually do.
