#policy-enforcement

#policy-enforcement

[Illegal ebikes] are a total menace on the road, the health minister, Mark Butler, said on Friday. Kids have done stupid things on bikes ever since the penny-farthing [but] the injuries that are coming into our hospital emergency departments are absolutely devastating. We've got to make sure we stop these things coming into the country [and] police are given the powers to crack down, to take them away, to crush them, to destroy them.

A key advantage of using OPA is its ability to decouple policy decisions from the business logic in your services. With OPA, the engine determines the outcome of a policy, while your application takes responsibility for enforcing it. This separation makes it possible to manage all policies in a single location, instead of having to update business logic across multiple systems-systems that may be written in different languages and maintained by different teams.

We have implemented technological measures to prevent the Grok account from allowing the editing of images of real people in revealing clothing such as bikinis.

What seems most likely: the law will not be rigidly enforced, as teen-agers and social-media companies figure out ways to circumvent the ban, but the social norm established by the law and its robust popularity among politicians and voters will lead to a significant downturn in social-media use by minors nonetheless. Not every fourteen-year-old is going to draw a moustache on their photograph or get a fake I.D.-and the law should be easier to enforce among younger kids,

People and institutions are grappling with the consequences of AI-written text. Teachers want to know whether students' work reflects their own understanding; consumers want to know whether an advertisement was written by a human or a machine. Writing rules to govern the use of AI-generated content is relatively easy. Enforcing them depends on something much harder: reliably detecting whether a piece of text was generated by artificial intelligence.

In conversations with creators this past week, we identified a few opportunities to provide more education around certain policies and what is not allowed on YouTube. For example, in the channel terminations we reviewed and upheld, we saw examples of creators mass uploading content with the sole purpose of gaining views, likes or other metrics; mass uploading auto-generated or low-value content; mass uploading content scraped from other creators with minimal edits; content misleading people into clicking off-platform;