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Governments and law enforcement must not race to adopt AI for security without first ensuring its governance is worthy of trust.
The future of AI in policing depends not just on what can be done, but on what should be done—and this requires legal courage, public engagement, and a steadfast commitment to democratic values.
A new initiative called CC Signals, designed to clarify how datasets may be reused in machine learning—helping shape an open, equitable AI ecosystem grounded in consent, reciprocity and legal clarity.
CC Signals is a proposed framework that allows dataset holders—ranging from individuals and academic institutions to large platforms—to specify the conditions under which their data can be used by AI.
While Meta secured a win, the court's opinion underscores that the victory was procedural and evidentiary—not a definitive declaration that AI training on copyrighted works is inherently lawful.
"It stands only for the proposition that these plaintiffs made the wrong arguments and failed to develop a record in support of the right one."
Situations in which large language models (LLMs), acting autonomously and with access to sensitive systems, pursue goals that come into conflict with human intentions...New Post
...even in scenarios that were meant to be benign or beneficial. These models may exhibit behaviors similar to a malicious insider: lying, blackmailing, leaking data, or sabotaging decisions.
What sets Bender apart is her resistance to techno-solutionism: the belief that all social, economic, or cognitive problems can be solved with more data and better algorithms.
Her activism stems from a belief that a small group of wealthy technocrats are reshaping society according to their own values—values she sees as exclusionary and extractive.
The CNIL’s new guidance on legitimate interest and AI development represents a mature and balanced attempt to reconcile AI innovation with European data protection values.
It acknowledges the practical necessity of large-scale data access—especially in a competitive global AI race—while reaffirming the GDPR’s protective core.
This paper makes a compelling case for aligning AI governance with the bedrock principles of copyright law—territoriality, clarity, and fairness.
If the EU wishes to lead in responsible AI, it must avoid trying to do so by stretching the limits of what its laws can credibly and lawfully govern.
OpenAI has drawn a clearer line between model training decisions and emergent ethical behavior. Researchers identified what they call a “misaligned persona” latent in the activation space of GPT-4o.
The question now is whether industry, regulators, and society will act on this knowledge before the next alignment crisis unfolds.
Verdict: Training LLMs on copyrighted books can be lawful—but only when done with care, legal acquisition, and respect for the limits of fair use.
For creators and AI developers, the message is clear: legal clarity demands documentation, licensing, and transparency—not just ambition. Future court cases will likely hinge on similar distinctions.
Is the "black box"—this zone of incomprehensibility at the heart of AI—an immutable technical reality that we must simply learn to manage?
Or is it, at least in part, a strategic narrative, a useful myth that serves commercial or ideological purposes? This report will deconstruct this paradox.