explainable ai

Artificial Intelligence General Education: Cognitive Autonomy in an Intelligent Society

This book develops a systematic framework for Artificial Intelligence General Education (AIGE) in response to the cognitive and social transformations of an intelligent society. It identifies cognitive autonomy—the capacity of individuals to actively regulate belief formation, evidence use, trust allocation, the bounda...

Investigating How AI Ethics Shape Perceived Advertising Credibility, Consumer Trust, and Purchase Decisions: A Systematic Literature Review

Abstract—The increasing use of Artificial Intelligence (AI) in digital advertising has created a new opportunity for businesses while also raising ethical concerns related to privacy, transparency, authenticity, manipulation, and algorithmic bias. These ethical issues may influence how consumers perceive advertising cr...

Scaling vision models does not consistently improve localisation-based explanation quality

Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. We investigate this relationship by evaluating 11 computer vision models representing increasing levels of depth and complexity within the ResNet, De...

AI, Law and Ethics in Countering Financial Crime

Artificial Intelligence (AI) is transforming the fight against financial crime, but how can it be deployed responsibly? In answer to that question, this book provides a comprehensive roadmap that integrates legal clarity, ethical guidance, and operational strategies on AI governance. Leveraging the EU's AI Act, nationa...

Who Chooses the Artwork? Curatorial Agency and Distributed Intent in Botto

Botto is often described as a decentralized autonomous artist, but its authorship cannot be located in image generation alone. This paper examines Botto as an agentic curatorial system in which generation, ranking, voting, feedback, and minting form a recursive loop. Drawing on Botto's documentation and prior accounts...

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

This work proposes a generational genetic algorithm to coordinate specialized large language model agents that integrate mechanistic arguments, reconsider assumptions, and assess evidence and testability that advances a vision of autonomous science in which AI research teams achieve a capacity for discovery beyond that...

Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks

As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and interconnected structural layer. Unlike previous network generations that mostly relied on centralized cloud analytics platforms, AI-native 6G ne...

Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details

This study proposes a formal definition of data storytelling in IML, introduces the DIST Pyramid to align data storytelling with IML, and presents the I-P-O Model to describe their interactions, and develops an architecture to explain AI decisions through distinct "What-if" and "Why-not" event-generation processes.

Trustworthy Agentic AI: A Comprehensive Cybersecurity and Systems Survey on Threat Landscapes, Defense Architectures, and Open Challenges

This survey systematically analyze threat surfaces across intra-execution loops and interaction planes, formulate a multi-layered zero-trust defense-in-depth architecture integrating Dual-LLM isolation, Capability-Based Access Control, kernel eBPF probes, and sandboxed runtimes, and map technical controls to internatio...

Explainable AI for Autonomous Systems: Interpretable Decision Making in Self-Driving and Intelligent Robots

Explainable AI for Autonomous Systems: Interpretable Decision Making in Self-Driving and Intelligent Robots

An Explainable Multimodal Deep Learning Framework for Alzheimer's Disease Classification Using MRI, PET, and Clinical Data

Abstract Alzheimer's disease (AD) often remains undetected until memory loss and cognitive decline become clinically apparent, at which point substantial neurological damage has already occurred. Early and reliable diagnosis therefore, remains one of the most pressing challenges in dementia care. This study presents a...