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11005
One of the hardest challenges in AI safety is finding the right balance: how do we protect people from harm without undermining their agency? This tension is especially visible in conversational systems, where safeguards can sometimes feel more paternalistic than supportive.
In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward.
โจ Transparency can make safety mechanisms into learning opportunities.
โจ Collaboration with diverse communities makes safeguards more relevant across contexts.
โจ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules.
Of course, this isnโt a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy.
Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward.
โจ Transparency can make safety mechanisms into learning opportunities.
โจ Collaboration with diverse communities makes safeguards more relevant across contexts.
โจ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules.
Of course, this isnโt a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy.
Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
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