Raw LLM Responses
Inspect the exact model output for any coded comment.
Look up by comment ID
Random samples — click to inspect
G
I recently changed the voicemail on my smartphone. I have two reasons why I'm cu…
ytc_UgwCWDgzx…
G
personally i use ai to make stock images and i check to make sure its not stolen…
ytc_Ugy-L9NHr…
G
AI should do boring stuff which people don't want to bother. For example it shou…
ytc_Ugy9xuWvk…
G
Maybe AI can figure out a way to get us musicians paid when people stream our fr…
ytc_UgzlMaALy…
G
I mean... Yeah. How many are left? Has thr percentage of animals poached slowly …
rdc_er9ydsi
G
After Microsoft laying off 7000 after saying 30% of it's coding is done by AI no…
rdc_mt7txc6
G
It will only the rich that own anything while everyone else struggles to survive…
ytc_UgxU0lIoh…
G
We are so ready to give our lives and creativity to AI because it makes it easie…
ytc_Ugyg72DQw…
Comment
By implementing logical rules and conditions, AI could have acted as an automated watchdog:
1. Preventative Controls:
• If a transaction exceeds a certain threshold, then it requires dual approval.
• When an approval chain is bypassed, flag it for review.
• How does this compare to normal transaction patterns?
2. Detection & Investigation:
• If a pattern of suspicious transactions emerges,
• Then trace them back to the decision-makers,
• When inconsistencies appear, cross-check supporting documentation,
• How does this align with past fraudulent cases?
By embedding these logic-based safeguards, AI could have eliminated loopholes before they were exploited. But even with strict rules, human manipulation can still find ways around them. From your experience with DOGE, would AI have been enough to stop fraud entirely, or would people always find creative ways to circumvent the system?
youtube
AI Governance
2025-10-03T10:2…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | developer |
| Reasoning | consequentialist |
| Policy | regulate |
| Emotion | indifference |
| Coded at | 2026-04-27T06:24:59.937377 |
Raw LLM Response
[
{"id":"ytc_UgxxQYlsZymChyVw19t4AaABAg","responsibility":"ai_itself","reasoning":"mixed","policy":"none","emotion":"mixed"},
{"id":"ytc_UgzKz_7QdsMw_OfnPGR4AaABAg","responsibility":"distributed","reasoning":"mixed","policy":"none","emotion":"mixed"},
{"id":"ytc_UgyLxljpKEfbwm3B5gt4AaABAg","responsibility":"unclear","reasoning":"deontological","policy":"unclear","emotion":"resignation"},
{"id":"ytc_UgzieOth2nDrY3_b2DR4AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"fear"},
{"id":"ytc_UgyKftSaUAOWRJ0fmXJ4AaABAg","responsibility":"company","reasoning":"unclear","policy":"none","emotion":"outrage"},
{"id":"ytc_UgyI2fuvUomiOXgKtvV4AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"fear"},
{"id":"ytc_UgxYqsltqBFOq5ZfVwB4AaABAg","responsibility":"government","reasoning":"deontological","policy":"regulate","emotion":"outrage"},
{"id":"ytc_UgyoLefoh89ONUBz1Kd4AaABAg","responsibility":"creator","reasoning":"deontological","policy":"regulate","emotion":"approval"},
{"id":"ytc_UgyNPWXW_pBeF9NibBF4AaABAg","responsibility":"developer","reasoning":"consequentialist","policy":"regulate","emotion":"indifference"},
{"id":"ytc_UgyOCJg43TEcZa_mkR54AaABAg","responsibility":"developer","reasoning":"mixed","policy":"regulate","emotion":"mixed"}
]