Key research themes
1. How can information ethics frameworks address emerging ethical challenges posed by AI, big data, and smart information systems?
This research theme focuses on developing and applying robust ethical frameworks and principles to manage the implications of artificial intelligence, big data, and smart information systems (SIS) in society. It investigates the challenges of algorithmic bias, fairness, transparency, accountability, privacy, surveillance, and power asymmetries that arise from the increasing use of these technologies. By integrating ethical theories with practical case studies and technological developments, this field aims to guide the responsible deployment of AI and SIS to ensure social, organizational, and environmental sustainability, and to promote equitable and human-centered data practices.
2. What are the conceptual foundations and evolving definitions of information ethics in the digital and information society?
This theme explores the philosophical origins, historical development, and conceptual frameworks underpinning information ethics as a discipline. It examines foundational concerns such as privacy, intellectual property, freedom of expression, truth, public space, and social epistemology, with attention to integrating classical philosophy, information science, media ethics, and contemporary challenges posed by digital transformation. The goal is to ground information ethics in coherent theoretical models that elucidate the moral implications of information dissemination, access, and control in the infosphere.
3. How can emerging technological architectures and standards enhance authenticity, transparency, and ethical oversight in digital authorship and information dissemination?
This theme investigates novel technological solutions and protocols designed to secure intellectual property rights, trace authorship, and promote transparency in digital content creation and distribution, especially amid the challenges posed by generative AI and decentralized systems. It addresses the problem of attribution loss due to remixing and AI regeneration, exploring mechanisms such as recursive encoding, cryptographic anchoring, and decentralized transparency frameworks that enable verification of origin, ethical accountability, and resistance to epistemic manipulation.