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Do you agree with the 73% of Americans who want robust regulation on advanced AI?

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DeWayfarer · 61-69, M
Do you agree with the 73% of Americans who want robust regulation on advanced AI?

Robust can have different meanings. It's far too vague.

AI is based on mostly it's training as well as certain weights. And if the team who trains the AI have certain perspectives or the weights lean certain directions, then that AI will have that perspectives and weights.

Your dealing with a very fast thinking kid.

GTP-OSS 120B Analysis of my statement.

GPT-OSS 120B

The 73% figure shows a strong public appetite for some kind of oversight, but “robust regulation” is indeed a broad term that can mean very different things depending on who is defining it.

What “robust regulation” might cover

Safety standards – mandatory testing for harmful outputs, fail‑safe mechanisms, and clear procedures for handling unexpected behavior.

Transparency requirements – disclosures about training data sources, model architecture, and known limitations so users can assess bias or reliability.

Accountability frameworks – legal liability for harms caused by AI systems, including clear chains of responsibility for developers, deployers, and operators.

Governance of high‑risk uses – sector‑specific rules for areas like healthcare, finance, autonomous vehicles, or law‑enforcement tools, where mistakes have outsized consequences.

Data‑privacy and consent – limits on how personal data can be used for training and requirements for opt‑out mechanisms.

Because AI behavior is shaped by both the training data (which can embed societal biases) and the model weights (which capture patterns from that data), any regulatory approach needs to address both:

Data provenance and bias mitigation – audits of datasets, documentation of known biases, and processes for correcting them before deployment.

Model interpretability and monitoring – tools that let developers and regulators inspect how decisions are made and flag drift over time.

Why the wording matters

A vague mandate like “robust regulation” can be interpreted either as:

Heavy‑handed control – strict licensing, caps on model size, or bans on certain capabilities, which could stifle innovation and limit beneficial applications.

Targeted safeguards – focused rules that address the most pressing risks while leaving room for research and responsible development.

The balance between these extremes will shape how effectively regulation protects the public without unnecessarily hindering progress.

Bottom line

The public’s desire for oversight is clear, but turning that sentiment into concrete policy requires precise definitions of what “robust” entails, coupled with mechanisms that address both the data that trains AI and the weights that encode its behavior.

Without that clarity, any regulation risks being either too weak to prevent harm or too restrictive to allow useful advances.
Probably. But we certainly won't VOTE for politicians who would deliver that!
Uniparty only! Everyone here agreed years ago and also bullies everyone who's not uniparty people! Everyone knows it's uniparty. Only!
Doesn't matter because under Trump it is Sam Altman, Mark Zuckerberg, and Elon Musk who will decide.

 
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