About this Research Topic
As a matter of fact, despite its links with fundamental advancements of science and the crucial contributions given to scientific progress in the most diverse research areas, AI is today increasingly seen as a tool to accomplish practical purposes. The ability of machine learning (ML) to make predictions and identify hidden patterns within vast floods of data has indeed enabled a range of technological applications that often overshadow the potential abilities of AI to improve the understanding of the world we live in.
The same is true in the legal field, where research in (machine-learning based) AI has mainly focused on practical purposes, spanning from enhanced information retrieval to document analysis and classification, from data-driven assessment of recidivism risk to supreme courts decisions' prediction. Less attention has been paid, so far, to the possibility of adopting AI for the implementation of more genuinely scientific research programs geared to explore how the law emerges from individual, social and institutional dynamics and how it is applied in these contexts.
Still, as shown by developments in socio-physics, computational social science, and complex networks theory, AI research can provide not only tools but also a conceptual vocabulary that has proven precious to deepen our understanding of social complexity of which the legal world is just an instance. Neural networks, agent-based simulations, evolutionary computing, multilayer networks, and higher-order interaction graphs could indeed shed new light on many of the building blocks underlying the complexity of the law, seen as a dynamic, emerging social construction.
The legal world has not, so far, fully taken advantage of the explanatory and scientific capabilities provided by AI. Some examples of the phenomena that could be explored through AI-driven methodologies are:
● implicit collective dynamics underlying norms emergence and evolution
● social dilemmas
● mechanisms tying individual decision-making with legally relevant social outcomes
● interplay between social/legal, informal/formal norms
● ethical challenges and accident responsibilities related to the emerging “smart” technologies (e.g., autonomous cars or domotics).
The stakes, it is worth emphasizing, are not only scientific but also include the possibility to build up better and more empirically grounded legal rules, policies, and institutions.
This Research Topic aims at providing the opportunity for an open and strongly interdisciplinary discussion, bringing to the table epistemological, scientific, and methodological challenges linked to the very idea of a science-oriented use of AI in legal and sociolegal research. The main goal is to bring into focus future research lines and topics at the borders between law and other disciplines.
Contributions are welcome from the most diverse research areas: computational legal theory, computational legal studies, philosophy of law, complexity science, computational social science, sociophysics, complex networks analysis, cognitive sciences. Papers should somehow tackle one of the following fundamental questions about the science-oriented use of AI in law:
● New/interesting research topics that may be addressed by exploiting AI for scientific purposes.
● Methodological issues (e.g., AI methods and their integration with other computational heuristics)
● Technologies and tools (e.g., simulation environments, analytical platforms)
● Epistemological issues (e.g., current and potential role of AI in natural and social science)
● Practical applications/spillovers (e.g., AI/science-enhanced regulatory impact analysis)
Keywords: AI, legal studies, sociolegal studies, AI-driven research methods, evolutionary computation, human society, institutions
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.