Skip to content
Log in

Author

Emmanuel Udoh

View on Semantic Scholar

3 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Conference Open access 2026

Epistemic Responsibility in AI-Enabled Societies: Ethics and Policy in the Era of Information Warfare

Artificial intelligence (AI) is fast changing civilian and military practice. Although the discussions of AI ethics often address issues of bias, fairness, and/or autonomous weapons, this paper suggests that the epistemic domain is the most urgent problem. Algorithmic manipulation and disinformation, as a kind of epistemic war, account to new ways of war discourses in the context of AI-enabled societies, where the possibility of responsible agency is put at risk. The main argument is that epistemic responsibility has to take priority over technological control in both ethics and policy. The paper shall then propose a new policy orientation that is based on principle of epistemic jus in bello : the principle of knowledge-based justness in warfare. This truth is that the integrity of knowledge environments is ethically no lesser urgent than defending civilian lives during kinetic conflict. By combining a formalized measure of epistemic integrity – via the Epistemic Integrity Index (EII) – with this normative framework, the paper bridges philosophy, ethics, and policy, offering a new model for understanding and regulating AI’s role in the epistemic dimension of modern conflict.

Abasianie Samuel Etuk, B. Stephen, Emmanuel Udoh et al. · 0 citations
Conference Open access 2026

Alignment-Free Identification of Microplastic Bioremediation Potential Using K-mer Frequency Patterns

To address the critical global challenge of microplastic pollution and the limitations of alignment-dependent genomic tools in analyzing fragmented environmental data, this study introduces an alignment-free computational framework for identifying microbial bioremediation potential. We established a standardized, multi-domain dataset encompassing Bacteria, Protists, Archaea, and Fungi, integrating metadata on polymer interactions to fill existing data gaps. Utilizing tetranucleotide frequency patterns (k=4), we developed a novel analysis method to isolate predictive genomic signatures, identifying C-rich motifs such as ‘CCCC’ as primary indicators of degradation capability.A statistical scoring model was subsequently implemented to rank candidate taxa, effectively prioritizing high-value organisms from complex metagenomes derived from plastic-associated microbial assemblages. Our approach demonstrates significantly reduced computational time and more sensitive than the older methods, identifying potential degraders that alignment techniques often miss. It connects the theoretical side of detecting functional genes with real-world environmental checks, creating a flexible tool to speed up finding new microbes for tackling plastic waste and restoring ecosystems.

U. Luke, Emem Okon Abang, Etimbuk Daniel Akpan et al. · 0 citations