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

A General Approach for Predicting the Behavior of the Supreme Court of the United States

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time evolving random forest classifier which leverages some unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.Comment: version 2.02; 18 pages, 5 figures. This paper is related to but distinct from arXiv:1407.6333, and the results herein supersede arXiv:1407.6333. Source code available at https://github.com/mjbommar/scotus-predict-v

Predicting Outcomes in Investment Treaty Arbitration

Crafting appropriate dispute settlement processes is challenging for any conflict-management system, particularly for politically sensitive international economic law disputes. As the United States negotiates investment treaties with Asian and European countries, the terms of dispute settlement have become contentious. There is a vigorous debate about whether investment treaty arbitration (ITA) is an appropriate dispute settlement mechanism. While some sing the praises of ITA, others offer a spirited critique. Some critics claim that ITA is biased against states, while others suggest ITA is predictable but unfair due to factors like arbitrator identity or venue. Using data from 159 final cases derived from 272 publicly available ITA awards, this Article examines outcomes of ITA cases to explore those concerns. Key descriptive findings demonstrate that states reliably won a greater proportion of cases than investors; and for the subset of cases investors won, the mean award was US$45.6 million with mean investor success rate of 35%. State success rates were roughly similar to respondent-favorable or state-favorable results in whistleblowing, qui tam, and medical-malpractice litigation in U.S. courts. The Article then explores whether ITA outcomes varied depending upon investor identity, state identity, the presence of repeat-player counsel, arbitrator-related, or venue variables. Models using case-based variables always predicted outcomes whereas arbitrator-venue models did not. The results provide initial evidence that the most critical variables for predicting outcomes involved some form of investor identity and the experience of parties’ lawyers. For investor identity, the most robust predictor was whether investors were human beings, with cases brought by people exhibiting greater success than corporations; and when at least one named investor or corporate parent was ranked in the Financial Times 500, investors sometimes secured more favorable outcomes. Following Marc Galanter’s scholarship demonstrating that repeat-player lawyers are critical to litigation outcomes, attorney experience also affected ITA outcomes. Investors with experienced counsel were more likely to obtain a damage award against a state, whereas states retaining experienced counsel were only reliably associated with decreased levels of relative investor success. Although there was variation in outcomes, ultimately, the data did not support a conclusion that ITA was completely unpredictable; rather, the results called into question some critiques of ITA and did not prove that ITA is a wholly unacceptable form of dispute settlement. Instead, the results suggest the vital debate about ITA’s future would be well served by focusing on evidence-based insights and reliance on data rather than nonreplicable intuition

Explanation for case-based reasoning via abstract argumentation

Case-based reasoning (CBR) is extensively used in AI in support of several applications, to assess a new situation (or case) by recollecting past situations (or cases) and employing the ones most similar to the new situation to give the assessment. In this paper we study properties of a recently proposed method for CBR, based on instantiated Abstract Argumentation and referred to as AA-CBR, for problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. In addition, we study properties of explanations in AA-CBR and define a new notion of lean explanations that utilize solely relevant cases. Both forms of explanations can be seen as dialogical processes between a proponent and an opponent, with the burden of proof falling on the proponent

Can Neuroscience Help Predict Future Antisocial Behavior?

Part I of this Article reviews the tools currently available to predict antisocial behavior. Part II discusses legal precedent regarding the use of, and challenges to, various prediction methods. Part III introduces recent neuroscience work in this area and reviews two studies that have successfully used neuroimaging techniques to predict recidivism. Part IV discusses some criticisms that are commonly levied against the various prediction methods and highlights the disparity between the attitudes of the scientific and legal communities toward risk assessment generally and neuroscience specifically. Lastly, Part V explains why neuroscience methods will likely continue to help inform and, ideally, improve the tools we use to help assess, understand, and predict human behavior

Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users for how they perceive and reason about fairness in algorithmic decision making. A key contribution of this work is the framework we propose to understand why people perceive certain features as fair or unfair to be used in algorithms. Our framework identifies eight properties of features, such as relevance, volitionality and reliability, as latent considerations that inform people's moral judgments about the fairness of feature use in decision-making algorithms. We validate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person's assessment of the eight latent properties of a feature in our exemplar scenario, we can accurately (> 85%) predict if the person will judge the use of the feature as fair. Our findings have important implications. At a high-level, we show that people's unfairness concerns are multi-dimensional and argue that future studies need to address unfairness concerns beyond discrimination. At a low-level, we find considerable disagreements in people's fairness judgments. We identify root causes of the disagreements, and note possible pathways to resolve them.Comment: To appear in the Proceedings of the Web Conference (WWW 2018). Code available at https://fate-computing.mpi-sws.org/procedural_fairness