TL;DR
This paper introduces 'Confident AI,' a framework for designing AI/ML systems that emphasize confidence in predictions through four core principles: Repeatability, Believability, Sufficiency, and Adaptability.
Contribution
It defines a new conceptual framework for AI confidence, integrating fundamental principles to enhance trustworthiness and reliability of AI systems.
Findings
Identifies four key principles for AI confidence.
Provides a foundational approach to improve AI trustworthiness.
Explores fundamental issues in current AI/ML systems.
Abstract
In this paper, we propose "Confident AI" as a means to designing Artificial Intelligence (AI) and Machine Learning (ML) systems with both algorithm and user confidence in model predictions and reported results. The 4 basic tenets of Confident AI are Repeatability, Believability, Sufficiency, and Adaptability. Each of the tenets is used to explore fundamental issues in current AI/ML systems and together provide an overall approach to Confident AI.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnomaly Detection Techniques and Applications · Data Stream Mining Techniques · Network Security and Intrusion Detection