artificial intelligence

Human-Centered Artificial Intelligence Framework for Overdose Mortality Surveillance, New York City, 2022‒2023.

Human-centered artificial intelligence models that identify suspected overdose deaths using narrative reports from medicolegal death investigations and describe ethical considerations for using AI in overdose surveillance are developed and evaluated.

Recursive diagnostic anchoring in conversational artificial intelligence: A two-process hypothesis of pseudo-corroboration in patient self-diagnosis

Comment on “Artificial Intelligence in Dental Education: Overview of Teaching, Assessment and Academic Performance Prediction”

Future Trends in AI, Machine Learning, and Big Data: Implications for Technical Leadership

The study finds that the technical leader must be prepared to constantly learn, adapt, and understand the technological and human factors to be effective in the AI era.

AI Agentic Architectures for Autonomous Data Engineering Pipelines

This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.

Economic impact and productivity modeling of AI agents

The paper provides a comprehensive analytical tool to make sense of micro and macro evidence, unpacks scenarios when AI agents will drive inclusive productivity growth, and outlines a policy roadmap focused on complementary investments, incentives for task-redesign, and workforce transition support measures.

Ethical challenges and bias mitigation in Artificial Intelligence systems

It is concluded that future research should prioritize interdisciplinary collaboration, robust regulatory frameworks, and continuous monitoring to promote the ethical use of AI.

AI-driven cybersecurity in software engineering

AI-driven cybersecurity in the software engineering field is discussed, where machine learning, deep learning, natural language processing, and reinforcement learning can be applied throughout the software development lifecycle to provide increased security.

Designing Self-Healing AI Agentic Systems: A Framework for Autonomous Detection and Response

A new scientific object – the Autonomous Recovery Efficiency Score (ARES) – is introduced – a quantitative measure of autonomous resilience, as well as a supporting foundation for future autonomous self-healing AI agentic infrastructure.

Cloud-based AI systems for scalable and intelligent software applications

The speed of cloud computing and artificial intelligence, which have transformed the way software applications are designed and deployed. The cloud-based AI systems provide a scalable, adaptable, and cost-efficient solution to build intelligent systems capable of processing large amounts of data and running complicated...

Security in Multi-Agent AI Systems: Modeling Emergent Vulnerabilities via Trust Graphs

This study offers new means to design a resilient multi-agent system that is able to resist emergent threats by formalizing trust as a dynamic security vector and introduces an Agentic Trust Dynamics Theory as a conceptual theory to model the evolution, loss, and impact of trust on emerging security vulnerabilities in...

The Post-Economic Human: Artificial Intelligence, Ontological Displacement, and the Ideological Foundations of Future Conflict

Artificial intelligence is usually analysed as a productivity technology, a labour-market shock, or a governance problem. This article argues that its deeper political significance may arise from a fourth channel: ontological displacement. Ontological displacement occurs when a technology weakens the socially recognise...