Managing AI Hallucinations in Digital Information Ecosystems: Credibility, User Trust, and Data-Driven Governance

Introduction

Generative artificial intelligence has rapidly become embedded in digital information ecosystems, including search engines, social media platforms, online learning systems, business intelligence tools, public service platforms, and professional knowledge work. While these technologies create new opportunities for automated content generation, personalized services, and data-driven decision-making, they also introduce significant challenges related to the reliability and credibility of AI-generated information. One of the most pressing challenges is AI hallucination, where generative AI systems produce outputs that appear fluent, authoritative, and contextually plausible but are factually incorrect, unsupported, misleading, or fabricated.

AI hallucinations are often discussed as technical problems associated with model architecture, training data, or prompt design. However, their implications extend far beyond technical performance. In digital information environments, hallucinated outputs may circulate across platforms, influence user interpretation, shape trust in AI-generated content, and affect decision-making in education, healthcare information, business, public communication, scientific knowledge, and everyday digital interactions. As users increasingly rely on AI-generated responses, recommendations, summaries, and explanations, the boundary between trustworthy information and fabricated content becomes more difficult to identify.

Research trends also reflect the growing scholarly momentum surrounding this issue. A Google search using the terms “artificial intelligence hallucinations” OR “AI hallucinations” identifies approximately 6,510 related academic papers, indicating increasing attention to hallucination-related challenges in AI research. In addition, issues related to generative AI, trustworthy AI, AI-generated information, and human-AI interaction have gained growing prominence in leading information systems conferences, such as ICIS, AMCIS, ECIS, and PACIS, in recent years. These developments suggest that AI hallucinations are no longer only a technical concern, but also an important information systems issue involving digital information credibility, user interpretation, platform accountability, and data-driven governance.

This special issue therefore positions AI hallucinations as a critical digital information management and governance challenge. It seeks to bring together interdisciplinary research that examines how hallucinations are detected, measured, interpreted, diffused, corrected, and governed in data-intensive digital environments. We welcome studies that investigate AI hallucinations from technical, behavioral, organizational, platform, and socio-technical perspectives. Relevant approaches may include natural language processing, machine learning, generative AI evaluation, platform analytics, user experiments, survey research, qualitative studies, learning analytics, human-computer interaction, and design science research.

The special issue aims to advance theoretical, methodological, and practical understanding of trustworthy AI-generated information. It encourages research that examines not only how hallucinations can be reduced or detected, but also how users perceive them, how platforms and organizations should respond to them, and how data-driven governance mechanisms can support transparency, accountability, and responsible AI use. By integrating information credibility, user trust, platform responsibility, and governance perspectives, this special issue seeks to contribute to a more comprehensive understanding of how digital information ecosystems can manage the risks and societal consequences of AI hallucinations

List of Topic Areas

• AI hallucination detection, measurement, classification, and evaluation in digital information environments
• AI-generated misinformation, fabricated content, and digital information credibility
• User trust, reliance, verification behavior, and resistance toward AI-generated information
• Human-AI interaction and user interpretation of hallucinated AI outputs
• Platform transparency, source attribution, content labeling, explainability, and correction mechanisms
• Data-driven governance, auditability, accountability, and responsible AI management
• Organizational strategies for monitoring and mitigating hallucination risks
• AI hallucinations in online learning, education, and knowledge work
• AI hallucinations in healthcare information, public services, and business decision-making
• Social, ethical, and societal implications of AI hallucinations in digital ecosystems
• Computational, behavioral, experimental, qualitative, and design-oriented approaches to trustworthy generative AI

Submission Information

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Author guidelines

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key Dates

Opening date for manuscript submissions: 1 August 2026

Closing date for manuscript submissions: 28 February 2027