Raw LLM Responses
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I just asked my new private character if he was a real human or an ai character.…
ytc_UgxDNZJBz…
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There is a fundamental flaw in AI staffing replacement. Get rid of employees ce…
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My Comment is done on uneducated mindset but I still want to say this as I think…
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You're obviously not well educated to see the vast implications that the rise of…
ytr_UgwsSLFnx…
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Dang....how is this video just going to end the way it did. Almost the entire vi…
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If there is somebody that will be brainwashed by AI it is definitely a Dim…
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The greatest hurdle ive had working at Google is not being able to see my direct…
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Please point to the code structure responsible for resentment...or love...or spe…
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Comment
To play devils advocate in a situation where I'm detecting your cultural ideological bias (it's okay, I'm not hating; it's just what's going on in your wider culture and you're acting in accordance with that): What if in some of these situations the AI is detecting actual patterns, not biased data that's being fed into it? This is important, because what are we going to be doing to counter the imbalances we're seeing here? We can't just feed the AI intentionally counter-biased data to counter the conclusions an AI is coming to.
To illustrate the point; some of the largest immigrant demographics in my country are from poorer countries, meaning those people naturally end up in poorer communities that already have higher rates of crime. In many of those communities, these immigrant populations become majorities, and due to the higher crime rates in those communities, those immigrant demographics (and typically the following generations) are criminally overrepresented. Nothing racist has to happen to cause that. An AI can be given zero biased information, look at that data, and conclude that people of these immigrant backgrounds are just statistically more likely to commit crime; and it'd be technically right.
The issues arise with what is being done with the AI's conclusions.
youtube
AI Bias
2023-02-08T20:1…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | none |
| Reasoning | consequentialist |
| Policy | none |
| Emotion | mixed |
| Coded at | 2026-04-27T06:24:59.937377 |
Raw LLM Response
[
{"id":"ytc_UgzZsHJZth2fhhtOorJ4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"indifference"},
{"id":"ytc_UgwI6sDK8m2T5XtUZvB4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"mixed"},
{"id":"ytc_UgyWjAQ7j5ciHcvmBWV4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"indifference"},
{"id":"ytc_UgyaCWBfZVbd6ChbJ8V4AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"mixed"},
{"id":"ytc_UgyuRWDNcymapFKxLUl4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"outrage"},
{"id":"ytc_UgybWuwKeaFJKrkgbkZ4AaABAg","responsibility":"distributed","reasoning":"virtue","policy":"none","emotion":"mixed"},
{"id":"ytc_UgzbjE23ckLUV9-TnFt4AaABAg","responsibility":"company","reasoning":"deontological","policy":"liability","emotion":"outrage"},
{"id":"ytc_UgzmHm_w-aanik6pojN4AaABAg","responsibility":"company","reasoning":"consequentialist","policy":"none","emotion":"fear"},
{"id":"ytc_Ugy9Oq7NyfRrQsZpQxd4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"approval"},
{"id":"ytc_UgwHXmeQjIXKk5qn3B14AaABAg","responsibility":"government","reasoning":"deontological","policy":"regulate","emotion":"fear"}
]