Types of AI Models

The ability to explain AI model decisions and recommendations is crucial for building trust in AI-derived decision-making processes. White Box models, with fewer rules and parameters, are simpler to understand and are more likely to be trusted. In contrast, Black Box models, which process millions or billions of input parameters with complex algorithms, are harder to explain. Cognitive Load Theory shows humans can understand models with up to seven rules, while Black Box models exceed this limit significantly.

Explainability Improves Trust

AI model developers are advised to use White Box solutions whenever possible. The KISS principle (Keep It Simple Stupid) should be followed. Black Box models require thorough documentation, data flow diagrams, and definitions. Using recognized best practices and frameworks can enhance the explainability of Black Box models. For example, medical devices have an ISO 14971 Framework for addressing explainability, as highlighted in a Med Device Online article. The National Institute of Standards and Technology (NIST) has also developed an explainability framework using four principles: explanation, meaningfulness, accuracy, and knowledge limits.

Transparency for Better Decision Making

Providing transparency and comprehension of AI models addresses ethical concerns in AI-derived decision making. Decision makers will better understand the AI model’s appropriate use, limitations, and inherent biases, allowing them to course-correct as necessary. This transparency and explainability lead to better decision-making and the avoidance of ethical challenges.

My Conclusion

In conclusion, transparency and explainability in AI models are essential for building trust in AI applications and improving decision-making processes. These principles empower leaders to navigate and avoid ethical concerns associated with AI.

Transparency and Explainability Improve Trust in AI Models. Image Generated by Dall-E.