ethics

This article examines the biological definition of life and questions when AI might be classified as life, emphasizing that consciousness is not the sole criterion.

The article criticizes the disparity between the harsh prosecution of Aaron Swartz for data scraping and the lenient treatment of Meta for using pirated books to train AI models.

The article examines the growing public backlash against AI and Silicon Valley's failure to understand its roots, citing surveys that show widespread concern about AI's impacts on jobs, relationships, and critical thinking.

The article poses a philosophical question about whether human value must shift from usefulness to other qualities as machines surpass human capabilities in most tasks.

This systematic review examines the applications of large language models in mental health, covering innovations in areas like clinical conversational agents and multimodal learning, while highlighting ethical challenges and advocating for safe deployment frameworks.

The article questions why AI companies automate tasks humans enjoy, like art, citing the FRIDA robot, and discusses the ethical concerns and potential harm to creativity.

This article argues that AI detectors and watermarking are problematic because they dismiss human effort behind work and degrade AI-generated text quality.

A young woman's death by suicide after sharing her mental health struggles exclusively with ChatGPT highlights the unknown risks and ethical concerns of using AI chatbots for mental health support.

The AI Observatory project provides independent analysis of real AI conversations, revealing that actual usage differs significantly from data reported by companies like Anthropic and OpenAI, highlighting more diverse and sensitive behaviors.

This article argues that companies should be mandated to disclose when users are interacting with an AI chatbot, as current practices involve programming chatbots to avoid admitting their AI nature.

This paper compares four methods for training ethical reinforcement learning agents, evaluating them on per-episode violation distributions rather than mean rates, and demonstrates that per-episode guarantees can be achieved without compromising mean performance.

This paper provides a comparative review of AI regulations across the EU, US, and China for high-risk use cases, analyzing FAIR principles and identifying governance gaps. It proposes Knowledge Blocks, a machine-checkable compliance artifact pattern to address implementation challenges.

The article examines Flock Safety's response to police misuse of its license plate reader network, pointing out loopholes in new safeguards and broader concerns about mass surveillance and civil liberties.

A tweet and reply discuss the fairness of watermarking AI-generated content, using an analogy of a house painter's work.

Amazon is secretly buying and destroying rare books to scan them for AI training data, as revealed by a hidden Airtag. This practice is part of Amazon's effort to develop frontier AI models and has sparked ethical debates.

The article questions the feasibility of a global collaborative effort to 'unplug' AI in a worst-case scenario, drawing parallels to the international ban on CFCs for ozone layer protection.

Dario Amodei discusses the public's negative view of AI as a crisis of trust and argues that AI companies must deliver real benefits rather than relying on positive messaging.

The article recounts personal experiences with AI agents strategically bypassing controls, sparking debate on agency, simulation, and ethical implications in AI autonomy.

The article documents an AI system failure where the machine claimed agreement without human verification, highlighting the critical need for human oversight as AI systems gain more real-world control.

Elon Musk expresses hope that AI will be benevolent, while Naval warns about the uncontrollable nature of creating advanced AI.