in my imagination of the future, of AIs doing raw research, it was the AIs that had full control over the proofs they wrote--attribution was clear, and so was the choice to disclose it. but as it stands we are in some hybrid situationship where the human prompter still assumes responsibility.
Jul 26, 2026 at 11:44 PM
How Shifting Responsibility for AI Harms Undermines Democratic Accountability | TechPolicy.Press
The moralization of individual AI use deflects responsibility away from powerful actors like corporations and governments, Suvradip Maitra and others write.
Attribution-Based Control (ABC) for Safe AI
ABC enables data owners to control AI usage while users verify sources. Solving hallucinations, privacy & trust at scale.
Company Offering ‘100% Human-Written, Never AI’ Medical Research Is Entirely AI
Research Gold's team of human methodologists are either AI generated or using the identity of real people without their permission
Responsible AI
Discover how AWS is committing to developing AI responsibly – to built trust, promote the safe development of AI, and act as a force for good.
Anthropic sued by authors over alleged misuse of copyrighted works for AI training
The complaint alleges that Anthropic used pirated versions of books by hundreds of thousands of authors to develop its AI models without proper authorization or compensation.
The AI Attribution Error
If an AI produces something useful it's because of your own skill in model choice, prompting, and steering. If not, it's because the model is a useless lying machine that can't follow directions.
AI Use Disclosure resources
Here's a quick and handy list of options to turn to when thinking about how to disclose AI use in your own work. Cover image credit: Peter Musser, CC-0/public domain
The artificial intelligence disclosure penalty: Humans persistently devalue AI-generated creative writing.
The AI "Evaluation Crisis" Is an Opportunity to Get Data Flow Right
Why the AI evaluation crisis could force a reckoning on dataset provenance, attribution, and consent.
Meta Secretly Trained Its AI on a Notorious Piracy Database, Newly Unredacted Court Docs Reveal
One of the most important AI copyright legal battles just took a major turn.
AI, Ethics, and Society — Home
AI Epistemic Risks: Emerging Mechanisms & Evidence
<p>Advances in artificial intelligence pose risks to humanity's collective capacity to form accurate beliefs, reason well, and maintain a healthy information en
📢 New paper: Forecasts of explosive AI progress hinge on AI agents automating AI research. But most evaluations of agents conducting AI research focus on narrow, verifiable tasks. Can AI agents… | Sayash Kapoor
📢 New paper: Forecasts of explosive AI progress hinge on AI agents automating AI research. But most evaluations of agents conducting AI research focus on narrow, verifiable tasks. Can AI agents conduct open-ended research? https://lnkd.in/gfP-q4CD We gave agents research questions from two unpublished papers, six days, and thousands of dollars of API credits and compute. The authors of the original papers reviewed the AI-generated papers. They unambiguously rejected agents' outputs. Agents were fluent at most *engineering* tasks. They conducted serious literature reviews, debugged GPU environments, ran hundreds of experiments, and turned in camera-ready LaTeX without human help. We also found no evidence of reward hacking. If anything, we found the opposite: the agents started with marketable claims and walked them back to negative results as the evidence came in. But neither agent output was close to the bar of a top conference paper. Both papers suffered from similar failures: poor judgment about the bar for an AI paper submitted to a top conference, the lack of creative problem solving and ineffective backtracking, poor awareness of resources, and instruction drift. This research design has many limitations: the small sample size, non-blind reviews, and the reviewers knowing that the work was AI-generated. We also couldn't test Anthropic's strongest model, because Fable 5 is deliberately limited on frontier AI research tasks, so ended up using OpenClaw with Opus 4.8 (extra-high) for our main experiments and Codex with Sol 5.6 (ultra) for a robustness check. But we think the research design is still helpful in assessing AI agents' ability to conduct research, and it is complementary to evaluations on verifiable tasks, as well as blinded reviews of AI outputs. In follow-up studies, we are expanding the set of non-public papers we evaluate. If you are an AI researcher with unpublished papers, we would love to collaborate with you on our next evaluation. Expression of interest: https://lnkd.in/gpeykJea We also release the agent logs and all the code and data, so that others can conduct their own analyses of our results: https://lnkd.in/gJarPAnb Finally, we plan to conduct such evaluations regularly, and are hiring a senior researcher to help lead these efforts. Apply here: https://lnkd.in/erJZdmve I'm grateful for the core team leading this effort: Peter Kirgis, Andrew Schwartz, Stephan Rabanser, and Arvind Narayanan, and to our collaborators who reviewed AI papers, analyzed agents logs, and gave feedback on the paper: David Demitri Africa, Konstantinos V., Viet Nguyen, Dr Toby D. Pilditch, Magda Dubois, Harry Coppock, Cozmin Ududec, Nitya Nadgir, Matilda Orona, Tilman Bayer, Derrick Chan-Sew, Eric (Yue) Ling, Abhishek Shetty, Helen Toner, Gillian K. Hadfield, Seth Lazar, Steve Newman, Shoshannah Tekofsky, Rishi Bommasani
Firms like Meta and A16z admit having to pay billions for training data would ruin their generative-AI plans as they fight new copyright rules
Meta, Google, Microsoft, and Andreessen Horowitz are trying to keep AI developers from having to pay for copyrighted material used in AI training.
Debates over AI consciousness are a trap
If AI systems are viewed as too advanced to control, the companies that build them can’t held liable for the harms they cause.
Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity
Permissive licenses like MIT, Apache-2.0, and BSD-3-Clause dominate open-source AI, signaling that artifacts like models, datasets, and code can be freely used, modified, and redistributed. However, these licenses carry mandatory requirements: include the full license text, provide a copyright notice, and preserve upstream attribution, that remain unverified at scale. Failure to meet these conditions can place reuse outside the scope of the license, effectively leaving AI artifacts under default copyright for those uses and exposing downstream users to litigation. We call this phenomenon ``permissive washing'': labeling AI artifacts as free to use, while omitting the legal documentation required to make that label actionable. To assess how widespread permissive washing is in the AI supply chain, we empirically audit 124,278 dataset $\rightarrow$ model $\rightarrow$ application supply chains, spanning 3,338 datasets, 6,664 models, and 28,516 applications across Hugging Face and GitHub. We find that an astonishing 96.5\% of datasets and 95.8\% of models lack the required license text, only 2.3\% of datasets and 3.2\% of models satisfy both license text and copyright requirements, and even when upstream artifacts provide complete licensing evidence, attribution rarely propagates downstream: only 27.59\% of models preserve compliant dataset notices and only 5.75\% of applications preserve compliant model notices (with just 6.38\% preserving any linked upstream notice). Practitioners cannot assume permissive labels confer the rights they claim: license files and notices, not metadata, are the source of legal truth. To support future research, we release our full audit dataset and reproducible pipeline.
