AI ethics on Wednesday, 1 January 2025

I have chosen not to talk in this Article about the warning that Rushdie is sounding in his essay In Good Faith, but to discuss more affirmatively the image of the modern self that he conveys. Still, I hope that we do not lose sight of the warning. The communitarianism that can sound cozy and attractive in a book by Robert Bellah or Michael Sandel can be blinding, dangerous, and disruptive in the real world, where communities do not come ready-packaged and where communal allegiances are as much

As Large Language Models (LLMs) become increasingly integrated into secure software development workflows, a critical question remains unanswered: can these models not only detect insecure code but also reliably classify vulnerabilities according to standardized taxonomies? In this work, we conduct a systematic evaluation of three state-of-the-art LLMs - Llama3, Codestral, and Deepseek R1 - using a carefully filtered subset of the Big-Vul dataset annotated with eight representative Common Weakne

The global energy transition, driven by the imperative to mitigate climate change, demands innovative solutions to address the technical, economic, and social challenges of decarbonization. Artificial intelligence (AI) has emerged as a transformative technology in this domain, offering tools to enhance each link in the energy system. This comprehensive review examines the current state of AI applications across key energy transition domains, including renewable energy deployment, energy efficien

Background and Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analyt

Artificial intelligence (AI) has been recently applied to different mental health illnesses and healthcare domains. This systematic review presents the application of AI in mental health in the domains of diagnosis, monitoring, and intervention. A database search (CCTR, CINAHL, PsycINFO, PubMed, and Scopus) was conducted from inception to February 2024, and a total of 85 relevant studies were included according to preestablished inclusion criteria. The AI methods most frequently used were suppor

Artificial intelligence (AI) and big data analytics are transforming healthcare globally and in Jordan. This study investigates the effects of AI and big data analytics on healthcare outcomes in Jordanian healthcare institutions. A comprehensive model is proposed to understand the antecedents of healthcare outcomes, including the impact of perceived ease of use, perceived usefulness, and organizational capabilities. Data were collected from 400 structured questionnaires, with a final sample size

• We examine the reasons for/against B2B managers’ intent to adopt generative AI (GenAI). • Need for uniqueness, information completeness, and convenience boost adoption intention. • Conversely, deceptiveness and information overload reduce adoption intention. • Managers’ intent to adopt GenAI is found to boost firm performance. • Ethical leadership acts as a moderator between intent to adopt GenAI and firm performance. This study examines key reasons (for and against) that influence business-to

Circulating tumor DNA (ctDNA) detection can predict clinical risk in early-stage tumors. However, clinical applications are constrained by the sensitivity of clinically validated ctDNA detection approaches. NeXT Personal is a whole-genome-based, tumor-informed platform that has been analytically validated for ultrasensitive ctDNA detection at 1-3 ppm of ctDNA with 99.9% specificity. Through an analysis of 171 patients with early-stage lung cancer from the TRACERx study, we detected ctDNA pre-ope

Artificial Intelligence (AI) is reshaping the legal landscape, with software tools now impacting various aspects of legal work. The intersection of Natural Language Processing (NLP) and law holds potential to transform how legal professionals, including lawyers and judges, operate, resolve disputes, and retrieve case information to formulate their decisions. To identify the current state of the applications of Transformers (also known as Large Language Models or LLMs) in the legal domain, we ana

Training and capacity building are essential for strengthening non-profit organizations and enhancing their ability to deliver impact. This book outlines foundational principles, methods, and tools for designing and implementing effective training programs. It includes strategies for assessing needs, engaging participants, and evaluating outcomes to ensure sustainable skill development.

Developing effective training programs requires careful planning, relevant content, and engaging delivery methods. This book provides a step-by-step guide for creating training initiatives in non-profit and mission-driven organizations, from initial needs assessment to final evaluation. It offers practical tools, templates, and case studies to ensure programs meet learning objectives and drive sustainable impact.

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiolog

Artificial intelligence (AI) scribe applications in the healthcare community are in the early adoption phase and offer unprecedented efficiency for medical documentation. They typically use an application programming interface with a large language model (LLM), for example, generative pretrained transformer 4. They use automatic speech recognition on the physician-patient interaction, generating a full medical note for the encounter, together with a draft follow-up e-mail for the patient and, of

OBJECTIVES: We aimed to evaluate the performance of multiple large language models (LLMs) in data extraction from unstructured and semi-structured electronic health records. METHODS: 50 synthetic medical notes in English, containing a structured and an unstructured part, were drafted and evaluated by domain experts, and subsequently used for LLM-prompting. 18 LLMs were evaluated against a baseline transformer-based model. Performance assessment comprised four entity extraction and five binary cl