Raw LLM Responses
Inspect the exact model output for any coded comment.
Look up by comment ID
Random samples — click to inspect
G
How?
What advantage is there to have, if you are able to prevent development al…
ytr_UgyLpLxuZ…
G
Lol untill the person says no i dont have the order number and the ai goes into …
ytc_UgxYq_P6C…
G
Hey Alex, ive been watching your videos for a while now and i cant help but thin…
ytc_Ugx1MjgWz…
G
Yes, we know what's coming. We know cameras are recording our faces & movement t…
ytc_UgxszUZuV…
G
it isn't transformative. AI "Art" literally cannot be copyrighted because its ma…
ytr_UgwW_oLB3…
G
good argument, as an artist also, there are still many other flaws of AI art tha…
ytr_Ugzh2U36v…
G
The comparison is not that far fetched. These automatons know nothing but the da…
ytr_UgwuuqGy_…
G
No matter if your an AI prompter or an artist
We are all equal, We all have the…
ytc_UgzQ10TId…
Comment
The term "bias" has different, but related meanings in statistics and machine learning. Since a lot of people learn statistics before they learn machine learning, I thought I'd point out how to relate the statistical meaning to the machine learning meaning. However, regardless of what oder you learn the concepts, here they are.
In statistics, bias refers to consistently over estimating or consistently under estimating. A model with high bias will make predictions that are (consistently) way higher or (consistently) way lower than they should be. A model with low bias will only be off by little bit in either direction.
In machine learning, bias refers to how well the model fits the training data. A model with high bias will have a poorly fitting model, and its predictions will be way off - but maybe not way off in a consistent way like when we talk about things in a statistical sense. A model with low bias will fit the data pretty well and the predictions will only be off by a little bit.
NOTE: "over estimating" is different than "over fitting". In fact "over estimating" is more closely related to "under fitting". If we consistently over estimate something, then our model can not be over fitting the data.
youtube
AI Bias
2020-04-13T18:2…
♥ 1
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | none |
| Reasoning | unclear |
| Policy | unclear |
| Emotion | indifference |
| Coded at | 2026-04-27T06:26:44.938723 |
Raw LLM Response
[
{"id":"ytr_Ugy0DAtLY9mrwXf_L-h4AaABAg.9FsVQgVfBoR9FuVNArWS","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_Ugy1Xyf8l5ei-znl26Z4AaABAg.9CWvWNurqny9CXpFzHBVsa","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"approval"},
{"id":"ytr_Ugy_FVI8-8up3Pn6Uwt4AaABAg.991xp5E2ooU99UvR31M7h_","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"mixed"},
{"id":"ytr_Ugw8-jz4mGyNdpgQX6h4AaABAg.97OjzA2wrnv97Ot9iNnLWh","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_Ugw8-jz4mGyNdpgQX6h4AaABAg.97OjzA2wrnv97P4kjd3_CG","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_UgyBGSa7ZL_DpSXgnZB4AaABAg.96phYbPX3GN96q2sUb0j9_","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_UgzaZQogfBQPFKvKK4p4AaABAg.96ZWOXkyIkQ96ZiPX2NeAb","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_UgzpejckazGIdWMwAEp4AaABAg.94YA8CfU0hp94YkGJjRErS","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_Ugyf4LmYOAFNEGVFgMV4AaABAg.94XRA_4kctd94_82WBUqEm","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_UgwMIA9cjPvOjrFTk0F4AaABAg.90-EtnredYt90-zcl2_hvD","responsibility":"none","reasoning":"unclear","policy":"unclear","emotion":"indifference"}
]