Raw LLM Responses
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G
11:53 i think its important to consider if someone is using ai as reference, the…
ytc_UgzDywziG…
G
It's unfortunate she has been so brainwashed by the CO2 fear mongers. CO2 has v…
ytc_UgxZMudDl…
G
paving the way for the antichrist system. already ceos of ai are lead by him, ho…
ytc_Ugy0IOSxG…
G
I'm making anti AI bracelets that say stuff like support real artists fuck AI or…
ytc_UgyjLI-9C…
G
These AI apocolypse people are nuts. There's no way 99% of people will lose thei…
ytc_Ugz0g-oPy…
G
That’s more of a corporation issue though. ideally you would have less people ne…
ytr_Ugx8KN7na…
G
We shot just blow a hole in everyone’s head who attempts to make AI at this poin…
ytc_UgyHt3gzh…
G
Why are we even testing and trying out ai art, and ai animation. We have people …
ytc_Ugx9uXdRR…
Comment
The point of the Google image search example isn't to accuse Google of some grave injustice, it's just an easy to understand example of how just because a computer is generating it doesn't mean its output isn't biased. The society it's getting its data from is biased in favour of female nurses, so it will return mostly pictures of female nurses even when the user is just looking for "nurse" without specifying gender. Once you understand that, it's easy to understand how that can become a problem when the situation is more complicated, the stakes are higher, which is the whole point of the episode.
Let's say there's 10 male nurses in the world and 90 female nurses. Out of those 100 nurses, one man and two women have committed the same misdemeanour on the job. Given that, would it be fair to make decisions on who to to employ as nurse based on the idea that 10% of men have committed this misdemeanour but only ~2% of women have? An AI trained with this data might. Worse yet, you don't even know it's doing this because its decision-making process is more or less a black box.
youtube
AI Harm Incident
2019-12-14T22:5…
♥ 9
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | distributed |
| Reasoning | consequentialist |
| Policy | none |
| Emotion | approval |
| Coded at | 2026-04-26T23:09:12.988011 |
Raw LLM Response
[
{"id":"ytc_UgwdzQf4Z81Wub_oBNh4AaABAg","responsibility":"none","reasoning":"deontological","policy":"none","emotion":"outrage"},
{"id":"ytc_UgzKdgOX1tqdrJ-LX8l4AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"regulate","emotion":"fear"},
{"id":"ytc_Ugx17723EZEsceZt_yp4AaABAg","responsibility":"unclear","reasoning":"consequentialist","policy":"unclear","emotion":"indifference"},
{"id":"ytc_UgwJjjxAxVRWcecmWyN4AaABAg","responsibility":"company","reasoning":"deontological","policy":"liability","emotion":"outrage"},
{"id":"ytc_UgyRuJzvS40auV0Pk7V4AaABAg","responsibility":"distributed","reasoning":"consequentialist","policy":"none","emotion":"approval"},
{"id":"ytc_UgwIFEfyAHEN7eFJOHF4AaABAg","responsibility":"unclear","reasoning":"unclear","policy":"unclear","emotion":"mixed"},
{"id":"ytc_UgzVtaD4ShO5brx3M9R4AaABAg","responsibility":"developer","reasoning":"consequentialist","policy":"regulate","emotion":"fear"},
{"id":"ytc_UgxFtDEwbaEIkOGAyr54AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"resignation"},
{"id":"ytc_UgzK-DjV2ISsCeBaM2B4AaABAg","responsibility":"distributed","reasoning":"consequentialist","policy":"regulate","emotion":"indifference"},
{"id":"ytc_UgyolKZJVkQldyGTCjh4AaABAg","responsibility":"unclear","reasoning":"consequentialist","policy":"unclear","emotion":"indifference"}
]