Why AI disclosure matters: the path to a proof is the value

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 28:47

   my question is like why does ai disclosure matter in research like if you couldn't distinguish if i used ai and i just publish some paper why do you care if i used it it's an excellent question um because i'll tell you why like as a a a theoretical computer scientist as someone sort of who is in this game right i care not just about what what is the answer to to to a question i care about how that answer was achieved, right? Because a person goes to a concert, they just want to hear the music, but if a professional musician goes, right, they want to know, like, you know, exactly what were the tricks, right? How could I do something like that, right? And so, you know, it's the same with math. You care not just about the destination, but about the journey, right? How did you get there okay and and part of that is just what techniques were needed within the proof okay

2. 29:48

   but i'm you know and this is even long before ai i was always very curious about the further question of how did these people find this proof right and and and unfortunately there's a long tradition in math that people will cover their tracks when they write a paper right they'll say like you know you know here's the proof you know we you know we now introduced the concept of a of a uh of a kappa comma gamma uh simplicial complex that you know obviously is the is the concept that you would want for this and with some convoluted definition even if you can follow all the steps mechanically like i i always want to know well what what path did you take to get to this, right? Because, you know, I know from experience that this kappa, kappa, comma, gamma simplicial complex didn't just arrive in your head, right? There were, you know, huge number

3. 30:47

   of false starts along the way. Like, you know, the author started from what was all, you know, from what they already knew in the literature, they tried some obvious things, the obvious things didn't work, and then that led them to something else, right? And, you know, I think in theoretically computer science, we do better often than in many other areas of actually explaining the path that we took, right? We have a tradition of writing like long introductions where you just sort of non-rigorously, you know, sort of explain, well, here is what you might first try. Here's why it doesn't work. So here's what we do instead, you know, and then that's kind of the appealing thing to read and then if you want the rigorous version and that's all kind of tucked into a long appendices right and so so i was but i i was always mostly interested in that in that first

4. 31:46

   part right in the the intuition part and in fact you know often rather than even reading that part i would rather just invite the author over to give a talk and then just sort of interrogate them during the talk and on understand like how did they actually come up with this okay and so for me you know wanting to know like did the author use ai how did they use ai is just of a part with that with wanting to know what was the path that they took right if the path involved ai well okay what what prompt did they give to an ai model right did they just one shot the entire problem or you know often it was more complicated than that like they had an idea you know and then and then they fed it to ai and then ai took the next step but it was wrong about something and then and then they had to point the

5. 32:39

   ai in a different direction like i i want to know the story i want to know what happened the analog in an experimental field would be uh you don't just want to know what was the result of the experiment right if you're another experimentalist you want to know the method you want to know how