This special issue addresses the challenges of governing content and ensuring safety in generative AI systems, particularly as large language models become increasingly prevalent. The issue seeks research on establishing trustworthy frameworks and mechanisms for managing content while developing safer generative AI technologies.

Appel publié par Information Processing & Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• December 31, 2026: Submission deadline

Brian King, Texas A&M University

Kyoung Jun Lee, Kyung Hee University

Danae Manika, Brunel University of London

Babak Taheri, Texas A&M University

This special issue seeks to advance understanding of how artificial intelligence can be designed and deployed responsibly and trustworthily within tourism and hospitality sectors. As AI becomes increasingly embedded in service delivery, marketing, operations, and guest experiences, the sector faces both opportunities for innovation and significant ethical challenges regarding fairness, accountability, transparency, and consumer trust.

The special issue invites interdisciplinary research examining the technical, managerial, legal, and societal dimensions of responsible AI adoption. Submissions should explore the design, governance, and impacts of AI-enabled platforms and systems, addressing topics such as algorithmic bias, consumer trust, sustainable AI applications, ethical frameworks, digital transformation, and labor implications within tourism and hospitality contexts.

Appel publié par Electronic Markets. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Ethical AI frameworks in tourism and hospitality: fairness, accountability, transparency, and inclusivity
• AI-driven consumer experiences: balancing personalization, convenience, and privacy
• AI robustness and risk: testing booking, pricing, and chatbot vulnerabilities, modeling threats, and strengthening resilience
• Trust, reliability and safety: building consumer trust, ensuring reliable performance, and managing risks
• Auditing AI systems: ongoing evaluations in pursuit of fairness, accuracy, and compliance
• Generative AI in marketing: pursuing responsibility in branding, engagement, and consumer journeys
• Sociotechnical and cultural dimensions: cross-cultural and historical perspectives on AI adoption
• AI, sustainability, and CSR: supporting or hindering responsible and sustainable practices in tourism and hospitality
• Future directions: conceptual frameworks and policy challenges for responsible and trustworthy AI in service innovation and resilience
• Social and labor impacts: the effects of adopting AI on employment, equity, and workforce well-being
• Frugal AI and digital twins: cost-effective and resource-conscious applications for operations and guest experiences
• Algorithmic transparency and rights: making AI decisions understandable and offering recourse for affected consumers
• Dark side of AI: risks of manipulation, surveillance, over-automation, and addictive design
• Corporate digital responsibility (CDR) in the age of AI within tourism and hospitality
• AI on digital platforms: exploring recommender systems, marketplaces, and platform governance
• Data ecosystems: sharing and leveraging user/usage data across digital travel and hospitality platforms
• Trustworthy AI principles: ensuring AI systems in tourism and hospitality are lawful, ethical, and technically robust
• Verification, validation, and explainability: designing AI that is interpretable and auditable for multiple stakeholders
• Trustworthy AI and consumer journeys: balancing automation with transparency and user empowerment

Timeline

• September 15, 2026: Submission deadline

This special issue focuses on the mathematical and computational foundations necessary for developing trustworthy artificial intelligence applications in transportation systems. As AI increasingly influences critical transportation decisions affecting safety, efficiency, and equity, there is an urgent need for rigorous mathematical frameworks that ensure these systems are reliable, interpretable, and accountable.

The special issue welcomes contributions that advance the theoretical understanding and practical implementation of trustworthy AI in transportation. This includes work on mathematical models that enhance transparency, ensure robustness against adversarial attacks, quantify uncertainties, and address fairness concerns in transportation algorithms and systems.

Submissions should demonstrate how mathematical and computational approaches contribute to building trustworthy AI systems that stakeholders can understand, verify, and depend upon for critical transportation applications.

Appel publié par Transportation Research Part B: Methodological. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Mathematical frameworks for explainability and interpretability in transportation AI
• Formal verification and validation methods for autonomous vehicles
• Trustworthy machine learning algorithms for traffic prediction and management
• Robustness and adversarial resilience in transportation systems
• Uncertainty quantification in AI-based transportation applications
• Fairness and bias mitigation in transportation decision-making systems
• Privacy-preserving techniques in transportation data analytics
• Game-theoretic approaches to multi-agent transportation systems
• Causal inference methods for transportation research
• Probabilistic and Bayesian methods in transportation AI

Timeline

• December 1, 2026: Full paper submission deadline