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The open-source AI debate: Why selective transparency poses a serious risk
It is misleading to call AI open source when no one can look at, experiment with and understand each element that went into creating it.
Perhaps as importantly, the transparency of open source allows for independent scrutiny and auditing of AI systems’ behaviors and ethics — and when we leverage the existing interest and drive of the masses, they will find the problems and mistakes as they did with the LAION 5B dataset fiasco. While this allows users to download and use the model at will, key components like the source code and dataset remain closed — which becomes more troubling in the wake of the announcement that Meta will inject AI bot profiles into the ether even as it stops vetting content for accuracy. Even as Anka Reuel and colleagues at Stanford University recently attempted to set up a new framework for the AI benchmarks used to assess how well models perform, for example, the review practice the industry and the public rely on is not yet sufficient.
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