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G I just did that with Meta AI. Ask them how many R's are in strawberry and they w… ytc_UgxuySTTr… G Ai is terrifying... there is literally an AI model that is not REAL making money… ytc_UgzKoom7E… G Anyone younger than 40 shouldn't be listened to with regards to LLMs because the… ytc_UgwGW2SXT… G I don’t think Geoffrey meant “to be a pumbler” literally, a robot with AI can de… ytc_Ugykh-a_T… G @jackfloof9465  it's an interesting perspective as long as you hold the same vie… ytr_UgzY-Cusj… G The fact that the chloride test assumes that the positive results is automatical… ytc_Ugy-0yl-i… G I'm disabled and so is my uncle. He is more disabled than me (in the way that I … ytc_Ugxmq9x7F… G Time to do my part and use my bad drawing to confuse AI even further.… ytc_Ugw6imvlI…
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
DimensionValue
Responsibilitydistributed
Reasoningconsequentialist
Policyregulate
Emotionindifference
Coded at2026-04-26T23:09:12.988011
Raw LLM Response
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