is a great resource for this. His research is on hate speech specifically. I know a bit about it, but I mostly work with large language models that are trained. They’re trained on many texts. If they want to do hate speech detection, they would have text labeled by people as hate speech versus not. Then they would train it to discriminate between the two.
When they're training models, the data they use might not have any of the voices we mentioned. Let’s say, the people writing about BLM or harassment only use Twitter data. People affiliated with BLM may not participate on Twitter because they're harassed. One of our co-authors, , did research showing that a lot of people talk about ageism. He found that many elderly people like to write blog posts for instance, or use other mechanisms and are not on social media. Do the people building these models have an additional step they can take to measure accuracy?When it comes to the technologists who create these models, it's not really a thing they consider. They might consider it, but also be like,"Ah, whatever. It's fine. We'll just do it,” kind of thing.
Legislators are controlled by companies. The companies pay lobbyists who have the time and resources to get laws passed that benefit companies. These companies also have money to pay armies of lawyers who harass whistleblowers day and night. Journalists have really been doing a great job of trying to hold them accountable, but if all parties don’t play their part, I don’t see how things will change. I think there needs to be a partnership between all stakeholders.
We call that diversity branding or ‘diversity theater.’ It does more harm than good because it brings us a lot more harassment., a professor and maybe the only Black woman who is a theoretical physicist, talks about her monthly security bill because of a publication called ‘Campus Reform’. They think there's this liberal agenda and they're out to get everyone. Death threats, arrests, all of that.
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