All things being equal, I'd recommend you publish in journals that are prestigious in your particular field (though it might not be worth the effort). In international relations / political science (which I know best) that might be e.g.: International Organization, International Security, American Journal of Political Science, PNAS.
Other journals that are less prestigious but more likely to be keen on AI governance work include: Nature Machine Intelligence, Global Policy, Journal of AI Research, AI & Society. There are also a number of conferences to consider: AIES, FAccT, workshops at big ML conferences like NeurIPS or ICML. Another thing to look out for is journals with AI governance/policy special issues.
I find that one good strategy for finding a suitable journal is looking for articles similar to what you want to publish and seeing where they've been published. You can then e.g. refer to those in your letter to the editors, highlighting how your work is relevant to their interests.
Overall, I think it's not that surprising that this change is being proposed and I think it's a fairly reasonable. However, I do think it should be complemented with duties to avoid e.g. AI systems being put to high-risk uses without going through a conformity assessment and that it should be made clear that certain parts of the conformity assessment will require changes on the part of the producer of a general system if that's used to produce a system for a high-risk use.
In more detail, my view is that the following changes should be made:
Goal 1: Avoid general systems being without the appropriate regulatory burdens kicking in. There are two kinds of cases one might worry about:
(i) general systems might make it easier to produce a system that should either be covered by the transparency requirements (e.g. if your system is a chatbot, you need to tell the user that) or the high-risk requirements, leading to more such systems being put on the market without them being registered.
Proposed solution: Make it the case that providers of general systems must do certain checks on how their model is being used and whether it is being used for high risk uses without that AI system having been registered or having gone through the conformity assessment. Perhaps this would be done by giving the market surveillance authorities (MSAs) the right to ask providers of general models about certain information about how the model is being used. In practice, it could look as follows: the provider of the general system could have various ways to try to detect whether someone is using their system for something high risk (companies like OpenAI are already developing tools and systems to do this). If they detect such a use, they are required to check that against the database of high risk AI systems deployed on the EU market. If there's a discrepancy, they must report it to the MSA and share some of the relevant information as evidence.
(ii) There’s a chance that individuals using general systems for high-risk uses without placing anything on the market will not be covered by the regulation. That is, as the regulation is currently designed, if a company where to use public CCTV footage to assess the number of women vs. men walking down a street, I believe that would be a high risk use. But if an individual does it, it might not count as a high risk use because nothing is placed on the market. This could end up being an issue, especially if word about these kinds of use cases spreads. Perhaps a more compelling example would be people starting to use large language models as personal chat bots. The proposed regulation wouldn’t require the provider of the LLM to add any warnings about how this is simply a chatbot, even if the user starts e.g. using it as a therapist or for medical advice.
Proposed solution: My guess is that the solution is that the provision suggested above is expanded to also look for individuals using the systems for high risk or limited risk uses and that they are required to stop such use.
Goal 2: (perhaps most important) Try to make it the case that crucial and appropriate parts of the conformity assessment will require changes on the part of the producer of the general system.
This could be done by e.g. making it the case that the technical documentation requires information that only the producer of the general model would have. It would plausibly already be the case with regards to the data requirements. It would also plausibly be the case regarding robustness. It seems worth making sure of those things. I don't know if that's a matter of changing the text of the legislation itself or about how the legislation will end up being interpreted.
One way to make sure that this is the case is to require that deployers only use general models that have gone through a certification process or that has also passed the conformity assessment (or perhaps a lighter version). I’m currently excited about the latter.
Why am I not excited about something more onerous on the part of the provider of the general system?
We've now relaunched. We wrote up our current principles with regards to conflicts of interest and governance here: https://www.governance.ai/legal/conflict-of-interest. I'd be curious if folks have thoughts, in particular @ofer.
Thanks for the post! I was interested in what the difference between "Semiconductor industry amortize their R&D cost due to slower improvements" and "Sale price amortization when improvements are slower" are. Would the decrease in price stem from the decrease in cost as companies no longer need to spend as much on R&D?
Thanks! What happens to your doubling times if you exclude the outliers from efficient ML models?
I really appreciated the extension on "AI and Compute". Do you have a sense of the extent to which your estimate of the doubling time differs from "AI and Compute" stems from differences in selection criteria vs new data since its publication in 2018? Have you done analysis on what the trend looks like if you only include data points that fulfil their inclusion criteria?
For reference, it seems like their criteria is "... results that are relatively well known, used a lot of compute for their time, and gave enough information to estimate the compute used." Whereas yours is "important publication within the field of AI OR lots of citations OR performance record on common benchmark". "... used a lot of compute for their time" would probably do a whole lot of work to select data points that will show a faster doubling time.
Thanks for this! I really look forward to seeing the rest of the sequence, especially on the governance bits.
Came here to say the same thing :)
Thanks for the question. I agree that managing these kinds of issues is important and we aim to do so appropriately.
GovAI will continue to do research on regulation. To date, most of our work has been fairly foundational, though the past 1-2 years has seen an increase in research that may provide some fairly concrete advice to policymakers. This is primarily as the field is maturing, as policymakers are increasingly seeking to put in place AI regulation, and some folks at GovAI have had an interest in pursuing more policy-relevant work.
My view is that most of our policy work to date has been fairly (small c) conservative and has seldom passed judgment on whether there should be more or less regulation and praising specific actors. You can sample some of that previous work here:
We're not yet decided on how we'll manage potential conflicts of interest. Thoughts on what principles are welcome. Below is a subset of things that are likely to be put in place:
Thanks! I agree that using a term like "socially beneficial" might be better. On the other hand, it might be helpful to couch self-governance proposals in terms of corporate social responsibility, as it is a term already in wide use.