Managing AI Hallucinations in Digital Information Ecosystems: Credibility, User Trust, and Data-Driven Governance
DETAILS
Call for Papers
Managing AI Hallucinations in Digital Information Ecosystems: Credibility, User Trust, and Data-Driven Governance
Journal: Data Technologies and Applications
Publisher: Emerald Publishing
Manuscript Submission Deadline: 28 February 2027
Data Technologies and Applications invites submissions for its Special Issue on “Managing AI Hallucinations in Digital Information Ecosystems: Credibility, User Trust, and Data-Driven Governance.”
About the Special Issue
Generative artificial intelligence is increasingly embedded across digital information ecosystems, including search engines, social media, online learning platforms, business intelligence tools, public services, and professional knowledge environments. While these technologies offer significant opportunities for automated content generation and data-driven decision-making, they also create serious challenges concerning the reliability and credibility of AI-generated information.
A major concern is AI hallucination, where generative AI systems produce information that appears credible and contextually appropriate but may be factually incorrect, unsupported, misleading, or fabricated.
This Special Issue examines AI hallucinations as not only a technical challenge but also an important information management, user trust, platform accountability, and governance issue. It seeks interdisciplinary research investigating how hallucinations are detected, measured, interpreted, circulated, corrected, and governed across digital environments.
The issue welcomes technical, behavioral, organizational, platform-based, and socio-technical perspectives. Research may employ natural language processing, machine learning, generative AI evaluation, platform analytics, experiments, surveys, qualitative methods, learning analytics, human-computer interaction, and design science approaches.
The broader objective is to advance understanding of trustworthy AI-generated information and develop effective mechanisms for transparency, accountability, verification, and responsible AI use.
Topics of Interest
Submissions may include, but are not limited to:
AI hallucination detection, measurement, classification, and evaluation
AI-generated misinformation and fabricated content
Digital information credibility and reliability
User trust, reliance, verification behavior, and resistance toward AI-generated information
Human-AI interaction and interpretation of hallucinated outputs
Platform transparency and source attribution
Content labeling, explainability, and correction mechanisms
Data-driven governance and AI auditability
Accountability and responsible AI management
Organizational strategies for monitoring and mitigating hallucination risks
AI hallucinations in online learning and education
AI hallucinations in professional knowledge work
AI-generated information in healthcare and public services
AI hallucinations in business decision-making
Social, ethical, and societal implications of AI hallucinations
Computational, behavioral, experimental, qualitative, and design-oriented approaches to trustworthy generative AI
Preferred Research Approaches
The Special Issue welcomes interdisciplinary contributions using approaches such as:
Natural Language Processing and Machine Learning
Generative AI Evaluation
User Experiments and Survey Research
Platform and Data Analytics
Human-Computer Interaction
Qualitative Research
Learning Analytics
Design Science Research
Behavioral and Socio-technical Research
Submission Information
Opening Date: 01 August 2026
Closing Date: 28 February 2027
Full manuscripts should be submitted through ScholarOne Manuscripts. During submission, authors must select the appropriate Special Issue title when prompted to choose the issue for their manuscript.
Authors must strictly follow the journal's official submission and formatting requirements.
Author Guidelines – Data Technologies and Applications
Submitted manuscripts must be original, must not have been previously published, and must not be under consideration elsewhere while under review.
Guest Editors
Chien-Liang Lin
Email: lin.chienliang@gmail.com
Martin J. Liu
Email: Martin.LIU@nottingham.edu.cn
I-Hsien Ting
Email: iting@nuk.edu.tw
Posted on ServiceSetu Academics — Premier Platform for Academic Opportunities & Research Collaboration.
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