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ZorbaTHut  2yr ago (text post)   45539 thread views

Culture War Roundup for the week of September 5, 2022

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bias the algorithms ahead of time

While anti-bias efforts are easy to abuse, I don't think they are inherently bad. There really is a bunch of detritus in the datasets that causes poorer results, e.g:

  • Generate anything related to Norse mythology, and the models are bound to start spitting out Marvel-related content due to the large amounts of data concerning e.g. their Thor character.

  • Anything related to the "80s" will be infected by the faux cultural memory of glowing neon colours everywhere, popular from e.g. synthwave.

  • Generating a "medieval knight" will likely spit out somebody wearing renaissance-era armour or the like, since artists don't always care very much about historical accuracy.

This can be pretty annoying, and I wouldn't really mind somebody poking around in the model to enforce a more clear distinction between concepts and improving actual accuracy.

People don't typically use the term "anti-bias" to reference fixing bias in the statistical sense. It nearly always means preventing an AI from making correct hate-fact predictions or generating disparate outcomes based on accurate data.

Examples:

  • Lending algos/scores (e.g. FICO) are usually statistically biased in favor of blacks and against Asians - as in, a black person with a FICO of X is a worse credit risk than an Asian person with the same FICO. This is treated as "biased" against blacks because blacks tend to have lower FICO scores.

  • COMPAS, a recidivism prediction algo, correctly predicted that "guy with 3 violent and 2-nonviolent priors is a high recidivism risk, girl who shoplifted once isn't". That's "biased" because blacks disproportionately have a lot more violent priors. (There's also a mild statistical bias in favor of blacks, similar to the previous example.)

  • Language models which correctly predict the % of women in a given profession (specifically, "carpenter" has high male implied gender, "nurse" high female implied gender, and this accurately predicts % of women in these fields as per BLS data) are considered "biased" because of that accurate prediction.

(Can provide citations when I'm not on my phone.)

All of the examples you describe are simply examples of "making more accurate predictions", and that is totally not what the AI bias field is about.

Of course like all lies there is a grain of truth. Bias is a real thing and it does degrade the usefulness of the models.

However I have absolutely no trust that in practice the usefulness being evaluated is to the user and not to the social movements of the activists.

I still believe in the ideals of free software, and I very much do not think anyone but myself is qualified to sort things on my behalf. Which is why I'm still clinging to RSS and configurable search engines.

Imagine living in a world where everything is sorted by the people who think /r/all is good. This is hell to me.

What does RSS have to do with software freedom?

With RSS, the user gets to do the curation and to modify the algorithm that does it if it is automated. Whereas large platforms today like Facebook, Twitter, etc hold a lot of power from being the only ones who can tweak the knobs of the algorithms that show most of the users the content they want to see.

My own personal experience of this is that I've thrown away my YouTube account and replaced it with a collection of channel feeds and now the content actually shows up instead of being eaten by the algorithm who decided that no, I don't get to see this video because it's badwrong.

User control over compute is I believe the cornerstone of free software, it's the very idea that underlies the freedoms, that the person running the software is in control, not the makers of the software or the software itself. I was told this by RMS in person.

Thanks, I'm an RMS fan too - but I never met him and don't think I will.

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