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this post was submitted on 23 Sep 2024
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Technology
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"Wow Johnson, no matter how much biased data we feed this thing it just keeps repeating biases from human society."
Sample input from a systematically racist society (the entire world), get systematically racist output.
No shit. Fix society or "tune" your model, whatever that entails..
Obviously only one of these is feasible from a developer perspective.
While it may be obvious to you, most people don't have the data literacy to understand this, let alone use this information to decide where it can/should be implemented and how to counteract the baked in bias. Unfortunately, as is mentioned in the article, people believe the problem is going away when it is not.
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In my mind, this is the whole purpose of regulation. A strong governing body can put in restrictions to ensure people follow the relevant standards. Environmental protection agencies, for example, help ensure that people who understand waste are involved in corporate production processes. Regulation around AI implementation and transparency could enforce that people think about these or that it at the very least goes through a proper review process. Think international review boards for academic studies, but applied to the implementation or design of AI.
AI ethics is a field which very much exists- there are plenty of ways to measure and define how racist or biased a model is. The comparison groups are typically other demographics.... such as in this article, where they compare AAE to standard English.