On your background knowledge comment, I agree that is an important open question (for this proposal, and other alignment techniques).
Related to that, I have been thinking through the systematic selection of which data sets are best suited for self-supervised pre-training of large language models—an active area of research in AI capabilities and Foundation Models more generally, which may be even more important for this application to legal data. For self-supervision on legal data, we could use (at least) two filters to guide data selection and data structuring processes.
First, is the goal of training on a data point to embed world knowledge into AI, or legal task knowledge? Learning that humans in the U.S. drive on the right side of the road is learning world knowledge; whereas, learning how to map a statute about driving rules to a new fact pattern in the real world is learning how to conduct a legal reasoning task. World knowledge can be learned from legal and non-legal corpora. Legal task knowledge can primarily be learned from legal data.
Second, is the approximate nature of the uncertainty that an AI could theoretically resolve by training on a data point epistemic or aleatory ? If the nature of the uncertainty is epistemic – e.g., whether citizens prefer climate change risk reduction over endangered species protection – then it is fruitful to apply as much data as we can to learning functions to closer approximate the underlying fact about the world or about law. If the nature of the uncertainty is more of an aleatory flavor – e.g., the middle name of the defendant in a case – then there is enough inherent randomness that we would seek to avoid attempting to learn anything about that fact or data point.
There are many other aspects of self-supervised pre-training data curation that we will need to explore, but figured I’d share a couple that are top of mind in the context of your world knowledge comment.
Public law informs AI more through negative than positive directives; and therefore it’s unclear the extent to which policy – outside of the human-AI “contract and standards” type of alignment we are working on – can inform which goals AI should proactively pursue to improve the world on society’s behalf. I agree with your comment that, “law tends to track situations where humans have conflicts of interest with each other, and it might not track universal values that are so obvious to everyone that conflicts of interest hardly ever arise.” This is a great illustration of the need to complement the Law Informs Code approach with other approaches to specifying human values. But I believe there are challenges with using the “AI Ethics” approach as the core framework, see section IV. PUBLIC LAW: SOCIETY-AI ALIGNMENT of the longer form version of this post, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4218031 . I think a blend of the frameworks could most fruitful.
Finally, it would be very interesting to conduct research on the possibility of “cross-cultural universals in legal systems that exemplify some common ground for human values,” and which domains of law have the most cultural overlap. There are many exciting threads to pursue here!
Regarding cross-cultural universals, I think there’s some empirical research on cross-cultural universals in which kinds of violent or non-violent crime are considered worst, most harmful, and most deserving of punishment. I couldn’t find a great reference for that in a cursory lit search, but there is related work on the evolutionary psychology of crime and criminal law that might be useful, e.g. work by Owen Jones: https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470939376.ch34
Hi Geoffrey, thank you for this feedback.
On your background knowledge comment, I agree that is an important open question (for this proposal, and other alignment techniques).
Related to that, I have been thinking through the systematic selection of which data sets are best suited for self-supervised pre-training of large language models—an active area of research in AI capabilities and Foundation Models more generally, which may be even more important for this application to legal data. For self-supervision on legal data, we could use (at least) two filters to guide data selection and data structuring processes.
First, is the goal of training on a data point to embed world knowledge into AI, or legal task knowledge? Learning that humans in the U.S. drive on the right side of the road is learning world knowledge; whereas, learning how to map a statute about driving rules to a new fact pattern in the real world is learning how to conduct a legal reasoning task. World knowledge can be learned from legal and non-legal corpora. Legal task knowledge can primarily be learned from legal data.
Second, is the approximate nature of the uncertainty that an AI could theoretically resolve by training on a data point epistemic or aleatory ? If the nature of the uncertainty is epistemic – e.g., whether citizens prefer climate change risk reduction over endangered species protection – then it is fruitful to apply as much data as we can to learning functions to closer approximate the underlying fact about the world or about law. If the nature of the uncertainty is more of an aleatory flavor – e.g., the middle name of the defendant in a case – then there is enough inherent randomness that we would seek to avoid attempting to learn anything about that fact or data point.
There are many other aspects of self-supervised pre-training data curation that we will need to explore, but figured I’d share a couple that are top of mind in the context of your world knowledge comment.
Public law informs AI more through negative than positive directives; and therefore it’s unclear the extent to which policy – outside of the human-AI “contract and standards” type of alignment we are working on – can inform which goals AI should proactively pursue to improve the world on society’s behalf. I agree with your comment that, “law tends to track situations where humans have conflicts of interest with each other, and it might not track universal values that are so obvious to everyone that conflicts of interest hardly ever arise.” This is a great illustration of the need to complement the Law Informs Code approach with other approaches to specifying human values. But I believe there are challenges with using the “AI Ethics” approach as the core framework, see section IV. PUBLIC LAW: SOCIETY-AI ALIGNMENT of the longer form version of this post, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4218031 . I think a blend of the frameworks could most fruitful.
Finally, it would be very interesting to conduct research on the possibility of “cross-cultural universals in legal systems that exemplify some common ground for human values,” and which domains of law have the most cultural overlap. There are many exciting threads to pursue here!
Thanks for this reply; it all makes sense.
Regarding cross-cultural universals, I think there’s some empirical research on cross-cultural universals in which kinds of violent or non-violent crime are considered worst, most harmful, and most deserving of punishment. I couldn’t find a great reference for that in a cursory lit search, but there is related work on the evolutionary psychology of crime and criminal law that might be useful, e.g. work by Owen Jones: https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470939376.ch34
Also David Buss (UT Austin) has written a lot about violent crime, esp. murder, e.g. https://labs.la.utexas.edu/buss/files/2015/09/Evolutionary-psychology-and-crime.pdf
Thanks!