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G This is eerie, the robot could have turned and shot the human. This AI is gettin… ytc_UgzKiHXVB… G @gondoravalon7540 Agreed. I'm not saying that the music industry is some kind o… ytr_UgxX-UuAE… G Actually, imagine OP didn't automate his job. Imagine he was working his ass off… rdc_hkg64ua G The ai is disgusting. I've done poisoning on my traditional arts. It annoys me s… ytc_UgwJa5YkP… G I didn’t vote in building this technology. These people have no more right than… ytc_UgwGHHHkt… G Everyone using filters that scan an alter your face, and then complain about the… rdc_hx16uj5 G Healthcare through your smartphone / smart watch. Constant monitoring of your vi… rdc_j1xtztb G That's a little bit vague. Let's just say AI cannot create anything original, on… ytc_UgwPO9Ube…

Comment

During the debate that followed ProPublia's accusations of the COMPAS-algorithm being discriminatory against black people, Kleinberg, Mullainathan and Raghavan showed that there are inherent trade-offs between different notions of fairness. In the case of COMPAS, for example, the algorithm was "well-calobrated among groups", which means that, independent of skin colour, a group of people classified as, say, 70% to recidive, actually had 70% of people that would recidive. However, ProPublia objected, that the algorithm produced more false positive predictions for blacks (meaning that blacks were labeled more often wrongly as high risk) and more false negative predictions for whites (meaning that whites were more often labeled wrongly as low risk). In their paper, the authors showed that these notions of fairness, namely "well balanced among groups", "balance for the negative class" and "balance for the positive class" are mathematically incompatible and exclude each other. One can't have the one and the other at the same time. So yes, AI-systems will be biased, as insisted upon in the video. But it raises questions about what kind of fairness we want to be implemented and what we're willing to give up.

youtube AI Harm Incident 2019-12-14T08:0… ♥ 46

Coding Result

Dimension	Value
Responsibility	distributed
Reasoning	consequentialist
Policy	regulate
Emotion	indifference
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"}
]

Raw LLM Responses