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20 Nw. J. Tech. & Intell. Prop. 309 (2022-2023)
Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans

handle is hein.journals/nwteintp20 and id is 282 raw text is: Copyright 2023 by Northwestern University Pritzker School of Law Volume 20, Number 3 (2023) Northwestern Journal of Technology and Intellectual Property LAW INFORMS CODE: A LEGAL INFORMATICS APPROACH TO ALIGNING ARTIFICIAL INTELLIGENCE WITH HUMANS John J. Nay* ABSTRACT - Artificial Intelligence (AI) capabilities are rapidly advancing. Highly capable Al could cause radically different futures depending on how it is developed and deployed. We are unable to specify human goals and societal values in a way that reliably directs Al behavior. Specifying the desirability (value) of Al taking a particular action in a particular state of the world is unwieldy beyond a very limited set of state-action-values. The purpose of machine learning is to train on a subset of states and have the resulting agent generalize an ability to choose high value actions in unencountered circumstances. Inevitably, the function ascribing values to an agent's actions during training is an incomplete encapsulation of human values and the training process is a sparse exploration of states pertinent to all possible futures. After training, Al is therefore deployed with a coarse map of human preferred territory and will often choose actions unaligned with our preferred paths. Law-making and legal interpretation convert opaque human goals and values into legible directives. Law Informs Code is the research agenda embedding legal processes and concepts in Al. Like how parties to a legal contract cannot foresee every potential if-then contingency of their future relationship, and legislators cannot predict all the circumstances under which their bills will be applied, we cannot ex ante specify if-then rules that provably direct good Al behavior. Legal theory and practice offer arrays of tools to address these problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations, i.e., to generalize expectations regarding actions taken to unspecified states of the world. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. * Fellow at Stanford University - CodeX - Center for Legal Informatics, operated by Stanford Computer Science Department and Stanford Law School, working on A Legal Informatics Approach to AI Alignment; Co-founder of Brooklyn Artificial Intelligence Research and its quantitative investment firm, Brooklyn Investment Group. 309

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