2 résultats pour « Explainable AI »

Differentiable Inductive Logic Programming for Fraud Detection

Explainable AI (XAI) is becoming increasingly important, especially in fields like fraud detection. Differentiable Inductive Logic Programming (DILP) is an XAI method that can be used for this purpose. While DILP has scalability issues, data curation can make it more applicable. While it might not outperform traditional methods in terms of processing speed, it can provide comparable results. DILP's potential lies in its ability to learn recursive rules, which can be beneficial in certain use cases.

An AI Vision for the Actuarial Profession

"The essay presents a vision of the AI-enhanced actuary, who leverages AI to build more accurate and efficient models, incorporates new data sources, and automates routine tasks while adhering to professional and ethical standards. We also discuss the challenges and speed-bumps along the way, including explainability, bias and discrimination risks, regulatory hurdles, and the need for actuaries to acquire AI knowledge and skills."