Publication: Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach
Date
2024
Authors
Alsalem M.A.
Alamoodi A.H.
Albahri O.S.
Albahri A.S.
Mart�nez L.
Yera R.
Duhaim A.M.
Sharaf I.M.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q?Rung Orthopair Fuzzy 2?Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q?Rung Orthopair Fuzzy 2?Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of ?4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed. ? 2023 The Author(s)
Description
Keywords
Artificial intelligence , Decision making , Health care , Linguistics , Sensitivity analysis , 2-tuple linguistic , Artificial intelligent , Distance-based , Health care application , Multi attribute decision making , Q?rung orthopair fuzzy 2?tuple linguistic combinative distance-based assessment , Q?rung orthopair fuzzy 2?tuple linguistic fuzzy-weighted zero-inconsistency , Rank ordering , Trustworthy , Benchmarking