Multi-agent blackboard architecture for supporting legal decision making

Our research objective is to design a system to support legal decision-making using the multi-agent blackboard architecture. Agents represent experts that may apply various knowledge processing algorithms and knowledge sources. Experts cooperate with each other using blackboard to store facts about current case. Knowledge is represented as a set of rules. Inference process is based on bottom-up control (forward chaining). The goal of our system is to find rationales for arguments supporting different decisions for a given case using precedents and statutory knowledge. Our system also uses top-down knowledge from statutes and precedents to interactively query the user for additional facts, when such facts could affect the judgment. The rationales for various judgments are presented to the user, who may choose the most appropriate one. We present two example scenarios in Polish traffic law to illustrate the features of our system. Based on these results, we argue that the blackboard architecture provides an effecive approach to model situations where a multitude of possibly conflicting factors must be taken into account in decision making. We briefly discuss two such scenarios: incorporating moral and ethical factors in decision making by autonomous systems (e.g. self-driven cars), and integrating eudaimonic (well-being) factors in modeling mobility patterns in a smart city

Formalizing value-guided argumentation for ethical systems design

The persuasiveness of an argument depends on the values promoted and demoted by the position defended. This idea, inspired by Perelman’s work on argumentation, has become a prominent theme in artificial intelligence research on argumentation since the work by Hafner and Berman on teleological reasoning in the law, and was further developed by Bench-Capon in his value-based argumentation frameworks. One theme in the study of value-guided argumentation is the comparison of values. Formal models involving value comparison typically use either qualitative or quantitative primitives. In this paper, techniques connecting qualitative and quantitative primitives recently developed for evidential argumentation are applied to value-guided argumentation. By developing the theoretical understanding of intelligent systems guided by embedded values, the paper is a step towards ethical systems design, much needed in these days of ever more pervasive AI techniques. Keywords Argumentation Ethical systems Teleological reasoning Value

Can Artificial Intelligence Interprete Legal Norms? A Problem of Practical Reason

La formalización del razonamiento jurídico y, específicamente, de la interpretación es un viejo sueño de nuestra cultura. Hoy, la Inteligencia Artificial parece lista para cumplir esa tarea. Teóricos computacionales y lógicos están desarrollando herramientas técnicas para estructurar modelos formales de interpretación jurídica útiles para la Inteligencia Artificial. Sin embargo, estos esfuerzos han conseguido sólo formalizaciones abstractas, que no son capaces de resolver cuestiones materiales sobre la respuesta correcta ante un caso nuevo. Los algoritmos no pueden descubrir, ni evaluar, problemas humanos sin la ayuda de programadores; no pueden decidir entre hipótesis interpretativas alternativas; en fin, la Inteligencia Artificial no puede cumplir con las exigencias de la razón práctica en el derecho.The formalization of legal reasoning and, specifically, of legal interpretation is an old dream of our culture. Today, Artificial Intelligence seems ready to comply with this task. Computational theorist and logicians are developing technical tools to structure formal models of legal interpretations useful to IA. However, these efforts have reached only abstract formalizations, but these do not have capabilities to resolve material questions about the correct answer in front of a new case. Algorithms cannot discover, nor evaluate, human problems without the help of programmers; they cannot decide between alternative interpretive hypothesis. At last, IA can not comply with exigencies of practical reason in law

Artificial intelligence as law:Presidential address to the seventeenth international conference on artificial intelligence and law

Information technology is so ubiquitous and AI's progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever

Value-based argumentation

Value-based argumentation is concerned with recognising, accounting for, and reasoning with, the social purposes promoted by agents’ beliefs and actions. Value-based argumentation frameworks extend Dung’s abstract argumentation frameworks by ascribing an additional property to arguments, representing the values they promote, and recognising audiences. Values are ordered according to the preferences of an audience (different audiences will have different preferences) and an attack is successful only if the value of the attacked argument is not preferred to its attacker by its audience. Arguments can be related to values through the use of an argumentation scheme, thus enabling us to structure value-based argumentation. We describe the motivation of valuebased argumentation, its formal description and properties, the argumentation scheme and its associated critical questions and some of the applications to which value-based argumentation has been put

Modeling Purposive Legal Argumentation and Case Outcome Prediction using Argument Schemes�in the Value Judgment Formalism

Artificial Intelligence and Law studies how legal reasoning can be formalized in order to eventually be able to develop systems that assist lawyers in the task of researching, drafting and evaluating arguments in a professional setting. To further this goal, researchers have been developing systems, which, to a limited extent, autonomously engage in legal reasoning, and argumentation on closed domains. This dissertation presents the Value Judgment Formalism and its experimental implementation in the VJAP system, which is capable of arguing about, and predicting outcomes of, a set of trade secret misappropriation cases. VJAP argues about cases by creating an argument graph for each case using a set of argument schemes. These schemes use a representation of values underlying trade secret law and effects of facts on these values. VJAP argumentatively balances effects in the given case and analogizes it to individual precedents and the value tradeoffs in those precedents. It predicts case outcomes using a confidence measure computed from the argument graph and generates textual legal arguments justifying its predictions. The confidence propagation uses quantitative weights assigned to effects of facts on values. VJAP automatically learns these weights from past cases using an iterative optimization method. The experimental evaluation shows that VJAP generates case-based legal arguments that make plausible and intelligent-appearing use of precedents to reason about a case in terms of differences and similarities to a precedent and the value tradeoffs that both contain. VJAP’s prediction performance is promising when compared to machine learning algorithms, which do not generate legal arguments. Due to the small case base, however, the assessment of prediction performance was not statistically rigorous. VJAP exhibits argumentation and prediction behavior that, to some extent, resembles phenomena in real case-based legal reasoning, such as realistically appearing citation graphs. The VJAP system and experiment demonstrate that it is possible to effectively combine symbolic knowledge and inference with quantitative confidence propagation. In AI\&Law, such systems can embrace the structure of legal reasoning and learn quantitative information about the domain from prior cases, as well as apply this information in a structurally realistic way in the context of new cases