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.· E3S Web of Conferences· 0 citations
The convergence of the Internet of Things (IoT) with Web3 ecosystems introduces new opportunities for automation, trust, and decentralized coordination. However, the same decentralized nature also amplifies security vulnerabilities, as conventional centralized intrusion detection and response systems are unable to provide real-time, tamper-proof protection at scale. This paper presents the Smart Contract-Based Automated Response System (SC-ARS), a novel framework that integrates blockchain smart contracts, machine learning (ML)-based anomaly detection, and automated mitigation policies. SC-ARS leverages lightweight consensus mechanisms and decentralized storage to ensure resilience against single points of failure, while smart contracts provide transparent and auditable enforcement of security actions. The ML pipeline, implemented using Random Forest, XGBoost, and LSTM models, is trained on benchmark datasets (NSL-KDD and CICIDS2017) to enable accurate anomaly detection. Experimental evaluation demonstrates up to 95% detection accuracy, a 50% reduction in response latency, and scalability to over 100,000 IoT devices without performance degradation. These results highlight the suitability of SC-ARS for deployment in smart cities, industrial IoT, and decentralized critical infrastructures where trust, transparency, and real-time responsiveness are essential.
S. Bassey, B. Stephen, Emediong Bassey Obot et al.· E3S Web of Conferences· 0 citations