Modelling Human Decision Behaviour with Preference Learning

Aggarwal, Manish; Tehrani, Ali Fallah

doi:10.1287/ijoc.2018.0823

Cited by 42 publications

(11 citation statements)

References 58 publications

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“…Preference learning combines decision-making and ML, focusing on a group of attributes or individuals and models multi-group learning functions with historical data. With EXAI, preference learning is widely used for linguistic models [60], [61], [62]. In conjunction with preference learning methods, DL methods are used to set and estimate parameters for decision models, identify optimal weights of multiple attributes for the linguistic model, and identify the parameter for fusion aggregation function and distributed data.…”

Section: J Data Driven Learning Methodsmentioning

confidence: 99%

Explainable AI for Healthcare 5.0: Opportunities and Challenges

et al. 2022

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In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It expands the operational boundaries of Healthcare 4.0 and leverages patient-centric digital wellness. Healthcare 5.0 focuses on real-time patient monitoring, ambient control and wellness, and privacy compliance through assisted technologies like artificial intelligence (AI), Internet-of-Things (IoT), big data, and assisted networking channels. However, healthcare operational procedures, verifiability of prediction models, resilience, and lack of ethical and regulatory frameworks are potential hindrances to the realization of Healthcare 5.0. Recently, explainable AI (EXAI) has been a disruptive trend in AI that focuses on the explainability of traditional AI models by leveraging the decision-making of the models and prediction outputs. The explainability factor opens new opportunities to the black-box models and brings confidence in healthcare stakeholders to interpret the machine learning (ML) and deep learning (DL) models. EXAI is focused on improving clinical health practices and brings transparency to the predictive analysis, which is crucial in the healthcare domain. Recent surveys on EXAI in healthcare have not significantly focused on the data analysis and interpretation of models, which lowers its practical deployment opportunities. Owing to the gap, the proposed survey explicitly details the requirements of EXAI in Healthcare 5.0, the operational and data collection process. Based on the review method and presented research questions, systematically, the article unfolds a proposed architecture that presents an EXAI ensemble on the computerized tomography (CT) image classification and segmentation process. A solution taxonomy of EXAI in Healthcare 5.0 is proposed, and operational challenges are presented. A supported case study on electrocardiogram (ECG) monitoring is presented that preserves the privacy of local models via federated learning (FL) and EXAI for metric validation. The case-study is supported through experimental validation. The analysis proves the efficacy of EXAI in health setups that envisions real-life model deployments in a wide range of clinical applications.

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Section: J Data Driven Learning Methodsmentioning

confidence: 99%

Explainable AI for Healthcare 5.0: Opportunities and Challenges

et al. 2022

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“…A typical example of the compensation aggregation function is the pure mean operator that returns a value representing an average of preferences. Aggarwal and Fallah Tehrani (2019) proposed a compensative weighted averaging (CWA) aggregation operator to model the human aggregation process, which is achieved by controlling an additional adjustable parameter

\lambda

. In this study, we apply the CWA operator in group decision‐making process to model compensation among multiple decision makers in a flexible parameterized way.…”

Section: The Stochastic Compromise Analysis Methodsmentioning

confidence: 99%

Compromise solutions for stochastic multicriteria acceptability analysis with uncertain preferences and nonmonotonic criteria

Liao

Lev

2021

Int Trans Operational Res

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As one of the most popular multiple criteria decision‐making (MCDM) methods, the stochastic multicriteria acceptability analysis (SMAA) is powerful in dealing with the uncertain preference information of decision makers. However, how to find compromise solutions for MCDM problems remains an open question in the SMAA family. A compromise solution refers to an alternative that is the closest to the ideal solution. In this paper, we propose an improved SMAA method, called the SMAA with compromise solutions (SMAA‐CO), to determine compromise solutions for MCDM problems with the uncertain preferences of decision makers and conflicting criteria. The SMAA‐CO uses nonmonotonic additive value functions to depict decision makers’ ideal values for reference points. We apply the proposed method to capture multiple decision makers’ uncertain preference information. An illustrative example concerning supplier selection in the pulp and paper industry is used to show the applicability of the proposed method. Some managerial insights are given.

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“…Every multi-criteria decision-making (MCDM) process has two stages: a criteria-based evaluation of alternatives, followed by their accumulation to identify the alternative with the top aggregation score, which informs the DM's choice (Aggarwal and Fallah Tehrani, 2019 ). MCDM methods allow for intentional conclusions to be made, as they can deal with the inherent complexity of many issues, as well as the understanding that results from the involvement of multiple participants (De Brito and Evers, 2016 ).…”

Section: The Challenge Of Developing Multi-criteria Decisions and Met...mentioning

confidence: 99%

Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

Černevičienė

Kabašinskas

2022

Front. Artif. Intell.

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The influence of Artificial Intelligence is growing, as is the need to make it as explainable as possible. Explainability is one of the main obstacles that AI faces today on the way to more practical implementation. In practise, companies need to use models that balance interpretability and accuracy to make more effective decisions, especially in the field of finance. The main advantages of the multi-criteria decision-making principle (MCDM) in financial decision-making are the ability to structure complex evaluation tasks that allow for well-founded financial decisions, the application of quantitative and qualitative criteria in the analysis process, the possibility of transparency of evaluation and the introduction of improved, universal and practical academic methods to the financial decision-making process. This article presents a review and classification of multi-criteria decision-making methods that help to achieve the goal of forthcoming research: to create artificial intelligence-based methods that are explainable, transparent, and interpretable for most investment decision-makers.

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Modelling Human Decision Behaviour with Preference Learning

Cited by 42 publications

References 58 publications

Explainable AI for Healthcare 5.0: Opportunities and Challenges

Explainable AI for Healthcare 5.0: Opportunities and Challenges

Compromise solutions for stochastic multicriteria acceptability analysis with uncertain preferences and nonmonotonic criteria

Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

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