arXiv:2607. 24275v1 Announce Type: cross Abstract: Ethical governance of AI-driven systems is often expressed through high-level principles and static documentation, creating a gap between regulatory requirements and system-level verification.
arXiv:2608. 02238v1 Announce Type: cross Abstract: Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health.
arXiv:2608. 15424v1 Announce Type: cross Abstract: The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patient data and supporting increasingly complex clinical decision-making.
arXiv:2608. 14562v1 Announce Type: new Abstract: AI governance is shifting from voluntary ethics to enforceable, risk-based regulation, yet cross-jurisdictional divergence creates compliance uncertainty for operators of high-stakes AI.
The paper introduces MIMIC-DOS, a dataset derived from MIMIC-IV that focuses on ICU cases where patient symptoms and medical signs are discordant. It presents CARE, a privacy‑compliant multi‑stage agentic reasoning framework that uses a proprietary LLM to generate structured categories and transitions, while a local LLM performs evidence acquisition and decision‑making. In retrospective evaluations on MIMIC‑DOS, CARE outperforms other LLMs and agentic workflows, demonstrating stronger handling of conflicting clinical evidence while preserving patient privacy.
arXiv:2608. 02699v1 Announce Type: new Abstract: When algorithms make or influence consequential decisions---about loan eligibility, hiring, or healthcare---EU law grants affected individuals a Right to Explanation.
arXiv:2406. 11868v2 Announce Type: replace-cross Abstract: The emergence of foundational models represents a paradigm shift in medical imaging, offering extraordinary capabilities in disease detection, diagnosis, and treatment planning.
Cognitive Digital Twins (CDTs) are dynamic computational models that represent an individual’s cognition, continuously updated with behavioral, contextual, or physiological data to predict or simulate that person’s mental processes or act as a proxy for communication and decision‑making. The paper defines CDTs, distinguishes them from related systems, and introduces a 5A governance framework—authority, autonomy, access & control, accountability, and availability—to address their unique risks. It identifies specific threats such as misrepresentation, epistemic authority shifts, shadow twins, and proxy‑power asymmetries, and proposes governance requirements for high‑risk CDTs, including stronger consent, purpose limitation, validity, traceability, contestation, independent review, and model retirement.
The article argues that current evaluations of large language models’ moral competence focus mainly on whether outputs align with human moral values—the so‑called moral value problem—while neglecting the moral norm problem, which concerns the models’ ability to identify and apply context‑sensitive moral norms. It attributes this imbalance to the field’s reliance on descriptive ethics frameworks that emphasize value representation over normative application. The authors review existing benchmarks, highlight three gaps—lack of ground‑truth norm data, insufficient evaluation of intermediate reasoning, and limited focus on context‑relevant features—and propose a research agenda to develop formal normative representations, expert‑annotated datasets, and evaluation protocols that distinguish between value‑level and norm‑level competence.
arXiv:2608.28593v1 Announce Type: new Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in...
The paper proposes rethinking bias in AI as a diagnostic tool rather than merely a flaw to be minimized. It introduces a multidimensional framework that examines bias across origin, lifecycle emergence, technical causes, and validation methods, covering 30 bias types, 16 verification methods, and 20 countermeasures for both traditional and generative AI. The authors present a hierarchical evidence framework distinguishing internal and external validity, and advocate for Ethics by Design principles to embed bias verification throughout the AI development lifecycle.
arXiv:2607. 10871v1 Announce Type: new Abstract: Contemplative traditions have long guided ethical behavior and prosocial interaction, and recent work suggests that contemplative principles (e.
The paper introduces a source‑grounded integrity gate for AI‑assisted personal health records, ensuring that data generated by large language models remains provisional until a deterministic monitor verifies it against the source document. The monitor only accepts candidates that contain a unique supporting quotation, appear within the same laboratory row, and preserve provenance, preventing the model from approving its own output. In Medical DataCloud, the system passed all 22 conformance and mutation tests and, in a replay of nine historical lab reports, admitted 72 of 97 numeric candidates while retaining 25 for human review.