The paper argues that the commonly cited gap between ethical principles and AI practice is not merely a matter of implementation or compliance, but arises from the complex institutional and professional contexts in which AI actors operate. Through interviews with designers and governors of anti‑trafficking AI systems, it identifies four interconnected moments—perception, judgement, situated action, and socio‑technical materialisation—through which ethical concerns are recognised, weighed, and either realised or sidelined. The authors contend that responsible AI governance must build epistemic infrastructures that transcend these constraints, enabling the knowledge of affected communities and potential harms to be effectively integrated into design and governance.
By Thao Do Ngoc, Emma Carmel
arXiv:2608. 11251v1 Announce Type: cross Abstract: Fairness in AI systems has become more important with recent regulatory demands, such as the EU AI Act.
By Ivan Luciano Danesi, Chiara Frigerio, Fabio Maccaferri, Giorgio Alessandro Motta, Pietro Zecca
arXiv:2608. 15326v1 Announce Type: new Abstract: Artificial intelligence (AI) benchmarks are not neutral tools of evaluation but socio-technical artefacts that shape competition, power, and research priorities within AI.
By Jason Branford, Angelie Kraft
The paper examines how the rise of AI capable of moral reasoning could reshape meta-ethics, traditionally focused on human ethics. It proposes a framework that identifies new questions about AI’s own ethics from both human and AI perspectives, dividing them into four domains. The author explores how existing meta-ethical theories might apply to these domains and argues that many human-centered formulations will need significant revision to accommodate AI.
By Shang Lu
The study tests deliberately biased AI assistants and finds that such bias improves human performance on tasks like misinformation evaluation, financial investment, and graduate education compared to neutral AI. However, participants undervalue biased AI and overvalue neutral AI, even when performance is similar. When two AI biases flank a participant’s perspective, performance gains are maintained while reducing the perceived cost and one‑sided influence.
By Shiyang Lai, Jiwoong Choi, Junsol Kim, Nadav Kunievsky, Yujin Potter, James Evans
arXiv:2607. 07612v1 Announce Type: cross Abstract: Artificial intelligence is rapidly evolving from generative systems to agentic AI capable of autonomously planning and executing tasks.
By Mubarak Raji, Masooda Bashir
arXiv:2508. 09219v3 Announce Type: replace-cross Abstract: Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by these technologies.
By Wilder Baldwin, Sepideh Ghanavati, Manuel Woersdoerfer
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.
By Samira Maghool, Paolo Ceravolo
arXiv:2607. 15480v1 Announce Type: new Abstract: As artificial intelligence (AI) systems increasingly impact society, ensuring their ethical and trustworthy deployment has become a global priority.
By Michael Papademas, Xenia Ziouvelou, Kostas Karpouzis, Vangelis Karkaletsis
arXiv:2606. 12420v1 Announce Type: cross Abstract: Our concepts of survival and self-interest were built for single, continuous biological lives.
By Dan Hendrycks
arXiv:2606. 16167v1 Announce Type: new Abstract: AI pluralism is often framed as a problem of representing diverse values, preferences, users, or outputs.
By Rashid Mushkani
arXiv:2608. 20041v1 Announce Type: new Abstract: Research on the agency of advanced artificial intelligence (AI) systems focuses on agency as a normative concept and on the agency of particularly agentic AI systems.
By Willem Fourie