Bartosz Brożek et al. explore the ethical and practical dilemmas arising from the integration of Artificial Intelligence (AI) in the realm of law. The authors suggest that despite the perceived opacity and unpredictability of AI, these machines can provide rational and justifiable decisions in legal reasoning. By challenging conventional notions of decision-making and justifiability, the paper reframes the discussion around AI’s role in law and provides a compelling argument for AI’s potential to aid in legal reasoning.
The authors delve into the intricacies of legal decision-making, highlighting the contrast between our traditional expectations and the realities of legal reasoning. They argue that while we expect legal decisions to be based on clearly identifiable structures, algorithmic operations on beliefs, and classical logic, the cognitive science research paints a contrasting picture. The authors further suggest that most legal decisions emerge unconsciously, lack a recognizable structure, and are often influenced by emotional reactions and social training. This observation paves the way for a paradigm shift, suggesting that rather than the process, it is the justifiability of the decision ex post that is paramount.
The authors propose a two-module AI system, one intuitive and the other rational. The intuitive module, powered by machine learning, recognizes patterns from large datasets and makes decisions. The rational module, grounded in logic, does not make decisions but justifies those made by the intuitive module. In this framework, AI can be seen as rational if an acceptable justification can be provided for its decisions, despite their unpredictability. This interesting intertwining of machine learning and logic reshapes our understanding of AI’s role in legal decision-making.
This paper touches upon broader philosophical issues surrounding consciousness, rationality, and decision-making. By arguing for a shift from a process-oriented to a result-oriented evaluation of decision-making, the authors challenge the traditional Kantian perspective. The proposed model, in which an AI’s decisions are assessed based on their post-hoc justifiability, aligns more closely with consequentialist philosophy. This emphasis on the end result rather than the means to reach it further stimulates the ongoing debate on the ethical implications of AI use and the re-evaluation of long-held philosophical tenets in the face of technological advancements.
Future research could explore various facets of this proposed two-module AI system, particularly the interplay and potential conflicts between the intuitive and rational modules. Questions around what constitutes an “acceptable justification” in various legal contexts also demand further exploration. Additionally, research could investigate how this approach to AI in law would intersect with other legal principles, such as fairness, transparency, and due process. Ultimately, the paper presents a compelling case for rethinking the role and evaluation of AI in legal decision-making, opening up intriguing possibilities for future philosophical and legal discourse.
Abstract
This paper addresses the black-box problem in artificial intelligence (AI), and the related problem of explainability of AI in the legal context. We argue, first, that the black box problem is, in fact, a superficial one as it results from an overlap of four different – albeit interconnected – issues: the opacity problem, the strangeness problem, the unpredictability problem, and the justification problem. Thus, we propose a framework for discussing both the black box problem and the explainability of AI. We argue further that contrary to often defended claims the opacity issue is not a genuine problem. We also dismiss the justification problem. Further, we describe the tensions involved in the strangeness and unpredictability problems and suggest some ways to alleviate them.
The black box problem revisited. Real and imaginary challenges for automated legal decision making