Musulmon Lolaev, Anand Paul*, Jeonghong Kim
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4707-4726, 2024, DOI:10.32604/cmc.2024.058675
- 19 December 2024
Abstract Evaluating artificial intelligence (AI) systems is crucial for their successful deployment and safe operation in real-world applications. The assessor meta-learning model has been recently introduced to assess AI system behaviors developed from emergent characteristics of AI systems and their responses on a test set. The original approach lacks covering continuous ranges, for example, regression problems, and it produces only the probability of success. In this work, to address existing limitations and enhance practical applicability, we propose an assessor feedback mechanism designed to identify and learn from AI system errors, enabling the system to perform the More >
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