Raw LLM Responses
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G
Maybe eventually, but that's less time that ai can spend getting better at steal…
ytr_UgwIUlk9u…
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@TheReal_JG oh absolutely those drivers are inept. but uh can they be held respo…
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@defaultuser7777 ai is not going to take away the human part of the justice syst…
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G
When you called out how people in support of AI say ‘you now have time for other…
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"no one should disagree with (the danger of AI". Says he in a world where peopl…
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I don't see this point brought enough. LLMs are very expensive! Some companies a…
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My most best, wise words. (And a thank you note for my fav yt channel noting abo…
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The NHTSA has investigated lots of self-driving systems. Tesla's has gotten lots…
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Comment
@claytonwoodcock6942 This system is not one of censorship; it's a system of AI clarification. Because it was not designed to censor, trying to use it to censor inevitably won't work very well. AIs don't know what's related or unrelated to what. So users correct them when their faulty automatic predictions...are faulty. That's all this is. I don't know why you're associating that with censorship. Removing opinions people don't like wouldn't be very effective. For example, no one is going to go and search "religion" and then report all the results related to Islam as not relevant just because they don't like Islam. It wouldn't be useful anyway, not only because it would happen on all sides of the topic (not just Islam), but also since the search term "religion" appears so often with the term "Islam". The AI would just retrain itself to associate the two. Plus, you'd have to go through thousands to billions of results to do this, since they are so strongly associated already, which is never going to happen. This system only works for relatively small exceptions to accuracy (as is intended), where the neural network doesn't have to change so many connections to correct the biases. All of these factors would apply to any opinionated search topic, not just religion and Islam.
youtube
AI Bias
2018-11-06T21:2…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | none |
| Reasoning | unclear |
| Policy | none |
| Emotion | approval |
| Coded at | 2026-04-27T06:24:59.937377 |
Raw LLM Response
[
{"id":"ytr_UgyvoP_seikcvfjwsox4AaABAg.8n6Cjgz1NSs8n7XmIOpYYg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgxeKGOPA1imTXCQOJp4AaABAg.8mi_xP7-zVi8mwWQZNsCns","responsibility":"user","reasoning":"deontological","policy":"none","emotion":"mixed"},
{"id":"ytr_UgxKPlkdr97pqxYQV2B4AaABAg.8mIss6bidRY8mJ3T6nxoli","responsibility":"ai_itself","reasoning":"consequentialist","policy":"ban","emotion":"fear"},
{"id":"ytr_UgzsLN7-edtE4kwqwUR4AaABAg.8mGY32p6H2e8mKwEOPQVpC","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"approval"},
{"id":"ytr_Ugz7tLFp_BWF6zxMgOR4AaABAg.8lftYiQlPz38lhS0y9Y5GG","responsibility":"company","reasoning":"deontological","policy":"liability","emotion":"outrage"},
{"id":"ytr_UgyPakM9sqTs3DwvZJp4AaABAg.8iSrTaUvamB8mxc6vOsdBQ","responsibility":"user","reasoning":"contractualist","policy":"industry_self","emotion":"indifference"},
{"id":"ytr_UgwORb2SFcSrXjuYppN4AaABAg.8fbJWBCUnE98lirVBwMSTp","responsibility":"company","reasoning":"deontological","policy":"regulate","emotion":"outrage"},
{"id":"ytr_UgyeELYxnQ_lkgPAsQx4AaABAg.8eWp2PI3KBB8nK0bRzg7_T","responsibility":"user","reasoning":"contractualist","policy":"industry_self","emotion":"indifference"},
{"id":"ytr_UgxfGQ0FY-V2K3B6qo94AaABAg.8dLTA2aJtMZ8nK16BKiKXv","responsibility":"user","reasoning":"contractualist","policy":"industry_self","emotion":"indifference"},
{"id":"ytr_UgxfGQ0FY-V2K3B6qo94AaABAg.8dLTA2aJtMZ8nK9BkKXNZF","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"}
]