This scoping review comprehensively covered ethical aspects of CAI in mental health care including evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in therapeutic contexts and its impact on care access, and addressing accountability.
A theoretical investigation of mechanisms by which ultraviolet circular polarisation may be produced in star formation regions, and how dichroic extinction may play a key role in producing an enantiomeric excess.
The semi-quantitative pfHRP2 device could improve current diagnostic work-up of severe febrile illness, which might consequently improve treatment choices, however, despite this recognized potential, several hurdles and drivers need to be taken into account when implementing this device in DR Congo.
It is argued that consciousness depends on the authors' nature as living organisms - a form of biological naturalism - and that real artificial consciousness is unlikely along current trajectories, but becomes more plausible as AI becomes more brain-like and/or life-like.
This paper proposes a framework grounded in collective intelligence and anchored in the foundational cognitive processes–reasoning, memory, and attention–to understand and engineer effective human–AI teams, and outlines design principles for achieving complementarity.
The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of applications, then explores questions around AI value alignment, well-being, safety and malicious uses, and considers the deployment of advanced assistants at a societal scale.
Delphi, an artificial intelligence system designed to predict human moral judgements based on John Rawls’s philosophical framework, is developed and tested, highlighting its potential for ethical applications and emphasizing the need to address its limitations and biases.
A method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness, and indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious.
It is argued that the public discourse is not simply a less complex way of speaking, but instead transcends its technical basis and introduces a non-exhaustive list of four central aspects of GenAI: (multi-)modality, interaction, flexibility, and productivity.
The transformation of educational environments from hierarchical instructionism to constructionist models that emphasize learner autonomy and interactive, creative engagement is discussed, providing insights for educators and policymakers seeking to harness digital innovations to foster adaptive, student-centered learning experiences.
It is argued that capacity cultivation (skilling) includes acquiring agential control over the capacities, inculcated through a long, gradual process of habituation, which calls for a critical reflection on the values inherent in AI socio-technical systems.
The research investigates epistemological and theological considerations related to the application of machine learning algorithms in interpreting sacred Islamic texts, proposing that AI applications adhere to principles of justice, transparency, and the preservation of human dignity and autonomy.
It is argued that GenAI systems function as "context machines" that must inherently address three interpretive challenges: situatedness, plurality, plurality, and ambiguity.
AI in current practice is deteriorating the authors' theoretical understanding of cognition rather than advancing and enhancing it, and this situation could be remediated by releasing the grip of the currently dominant view on AI and by returning to the idea of AI as a theoretical tool for cognitive science.
It is highlighted how the concepts of institutional agency and responsibility, administrative harms and burdens, and communicative blame provide theoretical resources that can help bioethics get closer to a more robust organizational ethics.
This study investigates uncertainty quantification in large language models (LLMs) for medical applications, emphasizing both technical innovations and philosophical implications, and proposes a comprehensive framework that manages both epistemic and aleatoric uncertainties.
It is essential to promote transparency, equity, and stakeholder involvement, and, therefore, to make sure that AI systems can support inclusive educational purposes, according to a systematic literature review based on the PRISMA 2020 framework.
This position paper argues that the ingredients are now in place to achieve openendedness in AI systems with respect to a human observer, and claims that such openendedness is an essential property of any artificial superhuman intelligence (ASI).
Recommendations are given of how developers, regulators, deployers and the public can navigate the relationship between AI hype, innovation, investment and scientific exploration, while addressing critical societal and environmental challenges.
The socio-technical nature of RAI limitations and the resulting necessity of producing socio-technical solutions are considered, bridging the gap between the theoretical considerations of RAI and on-the-ground processes that currently shape how AI systems are built.
It is argued that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction.
Insight from AI and cognitive science are combined to identify key commonalities and differences across three dimensions: notions of, methods for, and evaluation of generalization in AI and cognitive science.
The study develops a reframed understanding of Responsible AI as a dynamic, negotiated, and context-sensitive process and advances a composite theoretical model and a layered ecosystem framework that redistributes responsibility across design, deployment, governance, and public deliberation.
This workshop aims to develop a multidisciplinary community interested in exploring questions to protect against the erosion, and fuel the augmentation, of human cognition using GenAI.
It is found that image and text representations indeed share coarse semantic structure, but neither stronger language models nor richer captions yield fine-grained alignment, and arguably, full representational convergence would require fine-grained alignment.
A fragmented accountability landscape emerges, with regulatory frameworks lagging behind technological innovation, in which technological innovation frequently outperforms regulatory harmonisation and shared accountability structures.
The next intelligence explosion will not be a single silicon brain, but a complex, combinatorial society specializing and sprawling like a city, and by designing digital protocols, modeled on organizations and markets, the authors can build a social infrastructure of checks and balances.
This work introduces IslamicFaithQA, a 3,810-item bilingual (Arabic/English) generative benchmark with atomic single-gold answers, which enables direct measurement of hallucination and abstention and develops an agentic Quran-grounding framework (agentic RAG) that uses structured tool calls for iterative evidence seeking and answer revision.
The development of the neurodiversity movement is reviewed and the neurodiversity Framework in Medicine is proposed, which challenges traditional views by recognizing neurological differences as natural variations, advocating for inclusive, person‐centered approaches in healthcare.
This analysis explores how differences between AI systems and humans, such as the absence of conscious experience and immunity to fatigue, may affect an AI's capacity to fulfill relationship-specific functions and adhere to corresponding norms.
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