Survey on Explainable AI: From Approaches, Limitations and Applications Aspects

Yang, Wenli; Wei, Yuchen; Wei, H.; Chen, Yanyu; Huang, Guan; Li, Xiang; Li, Renjie; Yao, Naimeng; Wang, Xinyi; Gu, Xiaotong; Amin, Muhammad Bilal; Kang, Byeong Ho

doi:10.1007/s44230-023-00038-y

Cited by 44 publications

(7 citation statements)

References 166 publications

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“…Despite these advancements in AI-based weather forecasting models, several challenges persist in achieving 599 optimal performance and accuracy. (Yang et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Beyond the Horizon: A Critical Analysis of AI-based Weather Forecasting Models

Haji-Aghajany,

Rohm,

Lipinski

et al. 2024

Preprint

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This paper takes a comprehensive look at Artificial Intelligence (AI)-based weather forecasting models and focuses on the current status, challenges, and directions for further development. A review of nearly 40 models proposed primarily after 2015 highlights the importance of critically examining various aspects of AI-based weather forecasting models, including Machine Learning (ML) techniques, datasets, predictand parameters, extreme weather forecasting capability, lead time, spatiotemporal scale, performance criteria, and in-depth analysis of state-of-the-art models from different perspectives. Unlike previous reviews that have targeted only a limited number of models or features, this study focuses on different aspects of current AI-based models. Some important characteristics of AI-based models are computational efficiency and forecasting speed. However, challenges such as limited historical data and quality, model explainability, extreme weather forecasting, physical constraints, temporal adaptation, generalization, and uncertainty remain. Addressing these challenges is essential to enhance the effectiveness and reliability of AI-based weather forecasting across different weather conditions.

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“…Despite these advancements in AI-based weather forecasting models, several challenges persist in achieving 599 optimal performance and accuracy. (Yang et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Beyond the Horizon: A Critical Analysis of AI-based Weather Forecasting Models

Haji-Aghajany,

Rohm,

Lipinski

et al. 2024

Preprint

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“…This includes dealing with ethical issues related to data privacy and the potential misuse of AI-CDSS as well as addressing the lack of transparency and explainability in AIgenerated recommendations [106,107]. For example, trust has been associated with the system's capability to explain its decision-making process, emphasizing the role of explainable AI as a path to building trust in AI-CDSSs [94,108].…”

Section: Practical Implicationsmentioning

confidence: 99%

Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology (Preprint)

Dingel,

Kleine,

Cecil

et al. 2024

Preprint

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BACKGROUND Artificial Intelligence-enabled Clinical Decision Support Systems (AI-CDSSs) offer potential for improving healthcare outcomes, but their adoption among healthcare practitioners remains limited. OBJECTIVE The meta-analysis identifies predictors influencing healthcare practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and additional literature. METHODS The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7,731 results, of which 17 studies met the inclusion criteria. Random-effects meta-analysis, relative weights analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships of relevant predictor variables with the intention to use AI-CDSSs. RESULTS The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results show that performance expectancy (rc = .66), effort expectancy (rc = .55), social influence (rc = .66), and facilitating conditions (rc = .66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (rc = .63), trust (rc = .73), anxiety (rc = -.41), perceived risk (rc = -.21), and innovativeness (rc = .54) as relevant additional predictors. Trust emerged as the most influential predictor overall. The results of moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs compared to devices that combined diagnostic and treatment recommendations. Finally, the relationships between facilitating conditions and use intention was mediated through performance and effort expectancy. CONCLUSIONS The meta-analysis contributes to the understanding of the predictors of the intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and to explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for healthcare practitioners to ease the adoption of AI-CDSSs into their practice. CLINICALTRIAL https://osf.io/b4j3t

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“…Moreover, the literature highlights the importance of businesses adopting strong ethical frameworks to guide the development and deployment of AI systems. This involves establishing clear data usage policies, implementing transparency measures, and ensuring accountability for AI-driven decisions [34].…”

Section: Ethical Considerations and Regulatory Compliancementioning

confidence: 99%

“…Companies must devise strategies to ensure that their AI applications are practical and seen as fair and trustworthy by consumers. That includes dealing with algorithmic biases, ensuring data accuracy, and maintaining transparency in how AI systems make judgments [34]. The problem is to integrate AI technologies into their marketing and customer engagement strategies in the French market face challenges in effectively leveraging these tools to gain a competitive advantage while addressing integration complexities, ethical concerns, and alignment with consumer expectations.…”

Section: Introductionmentioning

confidence: 99%

Leveraging AI to Enhance Marketing and Customer Engagement Strategies in the French Market

2024

IJFMR

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This study investigates how businesses in France can leverage artificial intelligence (AI) technologies to enhance marketing and customer engagement strategies. The research problem centers on integrating AI into these strategies, which impacts businesses by offering the potential for a competitive advantage while posing challenges related to data privacy, ethical considerations, and customer expectations. The study aims to provide actionable insights and practical recommendations for effectively leveraging AI, guided by a conceptual framework emphasizing ethical AI practices, transparency, and continuous innovation. The research employs an integrative literature review (ILR) methodology to synthesize existing literature and analyze the opportunities and challenges associated with AI integration. The methodology involves problem formulation; data collection, evaluation, analysis, interpretation; and presentation of results. This paper reveals that AI significantly enhances personalized marketing, customer engagement, operational efficiency, and strategic decision-making by analyzing large datasets and identifying patterns in customer behavior. However, challenges such as GDPR compliance, algorithmic bias, and the need for transparency are prominent. The findings indicate that businesses can gain a competitive advantage by addressing these challenges and implementing recommendations such as creating job positions like Intelligent Ethics and Intelligent Data Protection Officers. The study highlights the importance of blending AI with human intuition and creativity to make well-rounded strategic decisions. It also emphasizes the need for comprehensive training programs in collaboration with academic institutions and AI companies to address the talent gap. The potential implications include improved marketing strategies, enhanced customer engagement, and sustainable growth. Recommendations for future research focus on exploring empirical studies to evaluate the long-term impacts of AI-driven marketing and customer engagement, as well as comparative studies to benchmark the effectiveness of AI-powered promotion and client interaction in various organizational settings.

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Survey on Explainable AI: From Approaches, Limitations and Applications Aspects

Cited by 44 publications

References 166 publications

Beyond the Horizon: A Critical Analysis of AI-based Weather Forecasting Models

Beyond the Horizon: A Critical Analysis of AI-based Weather Forecasting Models

Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology (Preprint)

Leveraging AI to Enhance Marketing and Customer Engagement Strategies in the French Market

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