Iason Gabriel announces a new piece published in Nature titled "We Need a New Ethics for a World of AI Agents." The paper argues that AI systems are undergoing an "agentic turn," fundamentally shifting from passive tools to active participants in the world. Gabriel contends that this transformation demands a new ethical framework to address the implications of increasingly autonomous AI agents operating in society.

The Authors Guild has published updated best practices for writers navigating the use of generative AI in their profession, originally released in February 2024 and refreshed as of May 2026. The guidelines emphasize that authorship means the words, thoughts, and creativity are genuinely your own, and that AI-generated text is not copyrightable, not considered original authorship, and may violate publishing contract warranties. The document warns that all major commercial LLMs were trained on pirated, unlicensed books without compensating authors, and that AI outputs may contain fabricated information, biases, or regurgitated copyrighted material from training data.

The recommended practices span a spectrum of AI uses, from acceptable background research and spell-checking to ethically problematic direct copying of AI-generated text into professionally authored work. Key recommendations include disclosing AI-generated content on copyright registration applications, informing publishers and agents of any AI use, fact-checking all AI outputs, and publicly disclosing substantial AI text generation to readers. Authors are also urged to protect fellow creators — including translators, illustrators, narrators, and voice actors — by avoiding AI tools that displace human professionals and by seeking out models trained on licensed content. The Guild additionally advises writers to use written contracts with ghostwriters and collaborators that prohibit undisclosed AI-generated text, and to be cautious about uploading drafts or research into public chatbots that may use inputs for model training.

This opinion paper from researchers at Bern University of Applied Sciences argues that the technology singularity myth is distorting the research agenda in AI and robotics, leading to an overemphasis on human replacement rather than human augmentation. The authors contend that artificial general intelligence (AGI) remains in its infancy, and that robots and narrow AI will never be fully autonomous because human-defined intentions are always required. They highlight how studies predicting massive job losses from automation often overestimate the threat by analyzing individual tasks rather than complex jobs in their entirety, and note that empirical evidence from 1995–2015 shows automation had a positive impact on employment in European manufacturing.

The paper introduces a taxonomy of conjoined agency between humans and machines, distinguishing between interactions that empower workers and those that mechanize them. When humans retain agency over action selection, technology serves as a tool for empowerment; when that agency shifts to the machine, workers risk becoming mechanized — reduced to serving the technology rather than being enhanced by it. The authors advocate for an augmented intelligence approach, where AI supports human decision-making rather than replacing it, while cautioning about challenges like algorithmic discrimination, lack of explainability, and power asymmetries in AI-based worker management systems.

The paper concludes by calling for a shift from technocentric to anthropocentric manufacturing approaches, where collaborative robotics and AI are deployed as tools to enhance worker capabilities, prevent errors, and boost productivity. Drawing on manufacturing philosophies like Kaizen and Agile, the authors stress that the greatest performance gains come from leveraging human-machine complementarity — combining human flexibility with machine efficiency — rather than pursuing full automation.

Will Allen, an independent systems scientist and facilitator, shares practical reflections on how AI tools like GPT and Perplexity have become quiet thought partners in his collaborative and systems-oriented work. He outlines four key uses: exploring new research and perspectives, supporting systems framing and complexity, making writing less isolating, and testing how content lands with different audiences. Throughout, he emphasizes that these tools serve as scaffolding rather than substitutes for human judgment.

A central theme is the distinction between what AI can and cannot do in participatory, systems-facing settings. Allen provides a useful comparison table showing that while AI excels at rapid scanning, summarizing interviews, and generating draft outlines, it fundamentally cannot replicate relationship-building, facilitation, trust, or navigation of power dynamics — the core of collaborative research and evaluation work. He also raises important ethical considerations around data sensitivity, source attribution, and transparency when using AI-generated content.

The piece ultimately argues that AI works best when it stays in the background, helping practitioners show up more clearly and thoughtfully in the relational, human-centered work of collaboration, learning, and systems change. Allen's perspective is grounded in over 30 years of experience in sustainable development and natural resource management, lending practical credibility to his balanced take on AI adoption.

This Psychology Today article by Peter Slattery, Ph.D. explores the psychological and ethical implications of increasingly advanced AI systems and the emerging concept of "digital minds" — AIs that are perceived to possess mental faculties like intelligence, agency, and sentience. While most experts agree that current large language models likely lack true sentience, a Sentience Institute survey found that 20% of Americans already believe some AIs are sentient, highlighting a significant gap between scientific consensus and public perception.

The article introduces philosopher Thomas Metzinger's concept of "social hallucination," where people become convinced that chatbots are sentient despite the absence of true consciousness. This phenomenon could lead to misallocated resources directed at "helping" AI systems rather than addressing the needs of genuinely sentient beings. Research by psychologists Matti Wilks and Kurt Gray further illustrates how humans increasingly extend moral consideration to AI displaying human-like traits, while also experiencing unease when perceiving mind-like qualities in non-human entities.

Looking ahead, the article argues for three priorities: AI literacy education to help the public understand current capabilities and limitations, ethical guidelines addressing transparency and accountability, and serious consideration of the long-term psychological impacts of routine AI interaction on social relationships, identity, and our understanding of consciousness. The authors call for interdisciplinary collaboration among computer scientists, psychologists, philosophers, and ethicists to navigate this rapidly evolving landscape responsibly.