explainable ai

Investigating AI/ML Models that Learn the Preferences and Constraints of Human Decision-Makers to Develop Interactive or Tools that Suggest Optimized Solutions While Allowing for Human Override and Incorporating Expert Judgment

The growing adoption of Artificial Intelligence and Machine Learning (AI/ML) systems in very complicated and sensitive decision-making areas have had a big impact on the issues of transparency, trust, and ethical accountability. The optimization-based AI systems were mainly focusing on maximizing the computational effi...

From Algorithms to Clinics: Evaluating AI’s Role in Pediatric Autism Diagnosis

From High-Level Ethics to Actionable Measures

The active evolution of technology has led to new changes, and one of the trends that has become topical and actively discussed within the academic community and society in general is Artificial Intelligence (AI). Nevertheless, it has turned out that the data on which AI systems are built is not always neutral. This ha...

Socially (dis)connected consumer trust in AI technologies: a systematic review and research agenda

Purpose Loneliness, as a mental health epidemic alongside the artificial intelligence (AI) improvements, has received attention in the literature. Motivated by the need to belong, lonely consumers experience psychological states that influence their trust in AI, resulting in inconsistent findings in this emerging resea...

Effects of Human-AI Voice Interaction in Sports Commentary on Perceived Credibility, Emotional Response, and Cognitive Recall

This study examines how commentary source (human vs. artificial intelligence [AI]), commentator gender, and accent shape perceived credibility, affective responses, and cognitive recall in sports broadcasting. A 2 (source) × 2 (commentator gender) × 2 (accent) between-subjects experiment (N = 867) showed that human-voi...

Measles and Rubella Surveillance in the Digital Era

The integration of digital technologies has profoundly transformed the surveillance of infectious diseases over recent decades. The emergence of approaches grounded in digital health, genomic surveillance, health information systems, and data analytics opens new perspectives for measles and rubella surveillance. The pr...

What survives? Semantic eligibility, distribution shift and metric interpretation in external validation of ADMET predictors

Machine-learning models for absorption, distribution, metabolism, excretion and toxicity (ADMET) are usually reported on benchmark splits drawn from the same curated collections used to train them. External validation is rarer, and when it is attempted, the assumption that a public dataset carrying a matching endpoint...

AI-Driven Underwater Surveillance for Maritime Security and Environmental Monitoring

The utilization of artificial intelligence (AI) is transforming underwater surveillance. It has made underwater surveillance smart, autonomous, and real-time. This chapter discusses the ways that AI technologies such as machine learning, deep learning, computer vision, and reinforcement learning are being incorporated...

Artificial Intelligence (AI)-Driven Remote Sensing and Predictive Analytics for Illegal Underwater Activity Detection

Although underwater ecosystems have many species and provide economic benefits, they are now being threatened by actions such as unregulated fishing, smuggling by sea, underwater attacks, and violations of boundaries. Older ways of surveillance find it tough to follow what happens in these big and complex waters due to...

Introduction

In this opening chapter, we set the stage for the book by describing both the opportunities and risks that artificial intelligence (AI) presents as it becomes increasingly embedded in critical domains such as healthcare, education, transportation, energy, conservation, and security. We argue that researchers and practi...

AI for Sustainable Energy Management

As renewable energy sources and electric vehicles proliferate, managing energy efficiently under uncertainty has become critical. This chapter focuses on optimization challenges in electric vehicle (EV) charging and vehicle-tobuilding (V2B) integration, formalizing them as sequential decision-making problems under unce...

Moral reasoning and responsible academic use of generative AI among Peruvian university students

The rapid expansion of artificial intelligence tools in higher education demands understanding whether students' moral development is associated with more responsible AI use. Moral development, conceptualized through Kohlberg's stage theory, provides a theoretically grounded framework for examining this relationshi...