Here's where we talk about whether AI is taking our jobs; it took me 5 years and three supervisors to produce a doctoral thesis that nobody will ever read.
Hurts to read. But I guess the scientific publications were already headed that way and AI accelerates mostly the unscrupulous. Now it's going to be harder and harder to separate the wheat from the chaff, and I fear AI will not help here, unless it's about purely theoretic results. I guess a web of trust will be more and more important in the future. Not a great solution though.
I think this is a problem that existed even before. If you look closely, AI just accelerates everything—both the pros and the cons. It seems to be an inherent problem with the academic system itself.
Signals are inherently bound to be distorted. The "legitimate methods" conceived by humans and social signals are easy to manipulate. Because public data like the number of published papers and diplomas act as strong social signals, they end up being distorted. This is known as Goodhart's Law.
The problem is that if this persists long-term, there will eventually be a shortage of personnel to quantitatively evaluate papers, and society probably won't hire these researchers. There is also no incentive to hire them, as verification rarely leads to direct profit. People tend to want to be the starting point of a discipline; they generally do not like checking other people's work.
As a result, academia will increasingly be validated solely by "which lab, which advising professor, and what peer network certified you." The value of the offline world will rise, simply because digital costs have become too cheap.
Soon, research quality and social status will likely merge again, resembling the Middle Ages. Even if we assume someone has actual research capabilities, if evaluators do not invest enough time in verification, the system will inevitably revolve around the more prestigious labs.
I suspect there will be a flood of research applying AI, but the topics of these studies will likely be quite narrow and localized (it would be nice to have statistics on this). I believe the overall diversity of topics explored across academia will diminish.
For this reason, I think existing networks will only grow stronger.
I feel that any solutions to this problem will go awry. I doubt self-purification is even possible anymore. Established researchers protect the monopoly power of publishers, while those existing publishers generally just want to cut costs. The authority of these publishers must be protected by the established researchers who have already secured their positions.
Next, the system that used to manipulate the number of papers will probably shift into a system that manipulates network reputation. The competition to get into good labs and the strategies to secure co-authorships with famous researchers will simply become the new means of competition.
This is a problem academics have talked about since at least the 1990s. You get what you incentivize, and we incentivize papers. Not innovation, not discovery, not new ideas, but papers.
Then what solutions has academia proposed? From what you're saying, it's been over 30 years.
In my country there's a saying: "A monk cannot shave his own head." Has it been solved? Or has this proverb, as expected, been proven true? I'm curious.
I think there's an equally large meta-incentive problem: the people who have power within the system are generally incentivised not to rock the boat, and the people who would like to rock the boat generally don't have power. This bind could be broken by external pressure (from government, itself incentivised by popular pressure) but it's very hard for most outsiders to understand and evaluate proposed changes, so even if the will was there, I don't know how much would be accomplished.
As an interested outsider myself, it's pretty depressing seeing how high the risk:reward ratio seems to be for people within academia who are driven to clean it up and call out bad actors, and how high the reward:risk ratio is for those who are willing to cut corners (or just plain cheat, if they do it in a minimally competent way).
Here's where we talk about whether AI is taking our jobs; it took me 5 years and three supervisors to produce a doctoral thesis that nobody will ever read.
Hurts to read. But I guess the scientific publications were already headed that way and AI accelerates mostly the unscrupulous. Now it's going to be harder and harder to separate the wheat from the chaff, and I fear AI will not help here, unless it's about purely theoretic results. I guess a web of trust will be more and more important in the future. Not a great solution though.
AI is pretty capable of detecting meaningless fluff though. If anything it could actually help to find the actually promising papers.
I think this is a problem that existed even before. If you look closely, AI just accelerates everything—both the pros and the cons. It seems to be an inherent problem with the academic system itself.
Signals are inherently bound to be distorted. The "legitimate methods" conceived by humans and social signals are easy to manipulate. Because public data like the number of published papers and diplomas act as strong social signals, they end up being distorted. This is known as Goodhart's Law.
The problem is that if this persists long-term, there will eventually be a shortage of personnel to quantitatively evaluate papers, and society probably won't hire these researchers. There is also no incentive to hire them, as verification rarely leads to direct profit. People tend to want to be the starting point of a discipline; they generally do not like checking other people's work.
As a result, academia will increasingly be validated solely by "which lab, which advising professor, and what peer network certified you." The value of the offline world will rise, simply because digital costs have become too cheap.
Soon, research quality and social status will likely merge again, resembling the Middle Ages. Even if we assume someone has actual research capabilities, if evaluators do not invest enough time in verification, the system will inevitably revolve around the more prestigious labs.
I suspect there will be a flood of research applying AI, but the topics of these studies will likely be quite narrow and localized (it would be nice to have statistics on this). I believe the overall diversity of topics explored across academia will diminish.
For this reason, I think existing networks will only grow stronger.
I feel that any solutions to this problem will go awry. I doubt self-purification is even possible anymore. Established researchers protect the monopoly power of publishers, while those existing publishers generally just want to cut costs. The authority of these publishers must be protected by the established researchers who have already secured their positions.
Next, the system that used to manipulate the number of papers will probably shift into a system that manipulates network reputation. The competition to get into good labs and the strategies to secure co-authorships with famous researchers will simply become the new means of competition.
This is a problem academics have talked about since at least the 1990s. You get what you incentivize, and we incentivize papers. Not innovation, not discovery, not new ideas, but papers.
Then what solutions has academia proposed? From what you're saying, it's been over 30 years. In my country there's a saying: "A monk cannot shave his own head." Has it been solved? Or has this proverb, as expected, been proven true? I'm curious.
I think there's an equally large meta-incentive problem: the people who have power within the system are generally incentivised not to rock the boat, and the people who would like to rock the boat generally don't have power. This bind could be broken by external pressure (from government, itself incentivised by popular pressure) but it's very hard for most outsiders to understand and evaluate proposed changes, so even if the will was there, I don't know how much would be accomplished.
As an interested outsider myself, it's pretty depressing seeing how high the risk:reward ratio seems to be for people within academia who are driven to clean it up and call out bad actors, and how high the reward:risk ratio is for those who are willing to cut corners (or just plain cheat, if they do it in a minimally competent way).
That is true in most systems!
Feels like this has been going on for 50 years. The AI acceleration just made it more visible.
Before it was artisanal non-replicable, p-hacked, publish or perish motivated, politically biased slop. Now we have some AI sugar sprinkled on top.