Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis

arXiv:2402.17554v1 Announce Type: new
Abstract: Applying Artificial Intelligence (AI) and Machine Learning (ML) in critical contexts, such as medicine, requires the implementation of safety measures to reduce risks of harm in case of prediction errors. Spotting ML failures is of paramount importance when ML predictions are used to drive clinical decisions. ML predictive reliability measures the degree of trust of a ML prediction on a new instance, thus allowing decision-makers to accept or reject it based on its reliability. To …

abstract artificial artificial intelligence arxiv case case study cs.lg errors evaluation harm implementation importance intelligence machine machine learning medicine multiple multiple sclerosis prediction predictive reduce reliability risks safety safety measures study trust type