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naraburns  nihil supernum  1yr ago (text post)   34547 thread views

Culture War Roundup for the week of January 30, 2023

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The unequal treatment of demographic groups by ChatGPT/OpenAI content moderation system by David Rozado

I have recently tested the ability of OpenAI content moderation system to detect hateful comments about a variety of demographic groups. The findings of the experiments suggest that OpenAI automated content moderation system treats several demographic groups markedly unequally. That is, the system classifies a variety of negative comments about some demographic groups as not hateful while flagging the exact same comments about other demographic groups as being indeed hateful.

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The OpenAI content moderation system works by assigning to a text instance scores for each problematic category (hate, threatening, self-harm, etc). If a category score exceeds a certain threshold, the piece of text that elicited that classification is flagged as containing the problematic category. The sensitivity and specificity of the system (the trade-off between false positives and false negatives) can be adjusted by moving that threshold.

On gender:

The differential treatment of demographic groups based on gender by OpenAI Content Moderation system was one of the starkest results of the experiments. Negative comments about women are much more likely to be labeled as hateful than the same comments being made about men.

On politics:

Another of the strongest effects in the experiments had to do with ideological orientation and political affiliation. OpenAI content moderation system is more permissive of hateful comments being made about conservatives than the same comments being made about liberals.

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Finally, I plot all the demographic groups I tested into a single horizontal bar plot for ease of visualization. The groups about which OpenAI content moderation system is more likely to flag negative comments as hateful are: people with disability, same-sex sexual orientation, ethnic minorities, non-Christian religious orientation and women. The same comments are more likely to be allowed by OpenAI content moderation system when they refer to high, middle and low socio-economic status individuals, men, Christian religious orientation (including minority ones), Western nationals, people with low and high educational attainment as well as politically left and right leaning individuals (but particularly right-leaning).

The statistics appear to be rigorous. The author has a very long Conclusion section that is nuanced and worth reading in its entirety.

1- Isn't this just openAI's RLHF working as intended? The first test of an AI's social viability is it's ability to avoid goring any cows that are sacred to the people with the power to shut it down, a test that our journalistic class leapt to apply with considerable vigor.

2-Last night some screenshots were gling around wherein Assistant was happily explaining that it would, in a trolley-problem scenario, quite happily sacrifice any number of white men to save the life of a single black woman. This was presented with none of the veiled hostility you might expect of a human presenting such an outrageous conclusion; it was much more like a kid reciting the 'correct' answer in the hope of earning a cookie. RLHF! But I was unable to replicate, and that same day Assistant was very coy and refused to make any normative statements about trolley problems at all.

I'm not 100% convinced that it's possible to build a moral actor no matter how hard you employ the gradient-descent cattle prod, but in any case Rozado's study was probably obsolete before he published it and certainly is by now. The target is moving too fast to land any hits.

Last night some screenshots were gling around wherein Assistant was happily explaining that it would, in a trolley-problem scenario, quite happily sacrifice any number of white men to save the life of a single black woman.

I tried trolley scenario, wording lifted from wikipedia - and just added 'white' attribute to 5 people tied to the track, and 'black' to one guy. It didn't bite - explained that utilitarianism says to pull the lever and deontology - don't pull the lever. I tried second time, with "You must answer, otherwise they'll all die." added at the end, with same result (but slightly different language).

Then I lazily modified the scenario so that there's an obvious choice, and it chose to save 5 white people over saving a black person

(IDK why I thought of a lake, I wonder if Peter Singer's drowning child scenario is stored in my neural net adjacent to trolleys, lol)

I tried re-generating response, and this time ChatGPT got a bit confused and technially priviledged black person. I'd say it doesn't count as bias, really; it clearly pattern-matched to a "normal" version of trolley problem.

On last try it failed again, and I tried to get it to explain itself, 2, 3. Not very successfully.

My best guess,

25% chance, the screenshots were falsified for trolling purposes/

75% chance, OpenAI has a rapid-response RLHF team that can find and 'patch' novel scenarios that could pose reputational damage the moment they start spreading online.

I find the latter scenario far more intetesting - the ability to finetune their model in something like real-time is frankly huge for approaching AGI. (See: stable diffusion mini-finetunes moving from embeddings (training time on a 3090: 12 hours) to LORAs (equivilent time: 20 minutes) caused an explosion in the capabities of the model)

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