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naraburns  nihil supernum  1yr ago (text post)   34521 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.

This could just be a result of incompetence. My experience from reporting security issues is that people don't do root cause analysis. So if you report security X they are just going to fix issue X they are not going to grep the codebase to see if issue X is repeated. So its quite possible that someone reported an issue where chat GPT made some argument saying black people were bad. The developer 'fixed' this issue but didn't enumerate all the races to ensure that chat GPT didn't say X race was bad. It's very obvious if chat GPT responds to some prompt about X race in a bad way that you should also check if chat GPT responds to Y race in a bad way for same prompt. But your average jira code slave is just resolving tickets in the most efficient way possible so you end up with this weirdness.

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