The charge prediction task aims to predict the charge for a case given its fact description. Recent models have already achieved impressive accuracy in this task, however, little is understood about the mechanisms they use to perform the judgment.For practical applications, a charge prediction model should conform to the certain legal theory in civil law countries, as under the framework of civil law, all cases are judged according to certain local legal theories. In China, for example, nearly all criminal judges make decisions based on the Four Elements Theory (FET).In this paper, we argue that trustworthy charge prediction models should take legal theories into consideration, and standing on prior studies in model interpretation, we propose three principles for trustworthy models should follow in this task, which are sensitive, selective, and presumption of innocence.We further design a new framework to evaluate whether existing charge prediction models learn legal theories. Our findings indicate that, while existing charge prediction models meet the selective principle on a benchmark dataset, most of them are still not sensitive enough and do not satisfy the presumption of innocence. Our code and dataset are released at https://github.com/ZhenweiAn/EXP_LJP.

The problems with Clearview AI's facial recognition system, particularly in the hands of police, are myriad and serious. That the technology exists as it does at all raises significant ethical concerns, and how it has been used to feed people into the criminal justice system raises significant due process ones as well. But an article in the New York Times the other day might seem to suggest that it perhaps also has a cuddly side, one that might actually help criminal defendants, instead of just hurting them. But don't be fooled – there is nothing benign about the facial recognition technology pushed by Clearview AI, and even this story ultimately provides no defense for it. It was not the hero here, because the problem it supposedly "solved" was not the problem that actually needed solving.

In the modern banking world new technologies play a widely reported role in anti-money laundering (AML) protocols preventing financial crime – however it is important that we do not overlook technology's potential for establishing financial innocence. To businesses and institutions operating in and between developed markets, whose international transactions are fast and painless, this sentiment may seem counter intuitive. AML compliance is necessary for regulatory reasons, and catching out bad actors is, of course, a primary goal of any business – but why should we view AML technology through the lens of establishing innocence? This is a question which emerging market corporates will have no difficulty answering if they have ever attempted to interface with counterparts in developed markets. Entities based in emerging markets are often tarred with the brush of AML risk due to their geography and unrelated to their specific business, and – consequently – such organisations find international transactions lengthy, arduous and expensive as they navigate an AML compliance process that operates from a base level that is an unfair assumption of their risk.

Applying automated reasoning tools for decision support and analysis in law has the potential to make court decisions more transparent and objective. Since there is often uncertainty about the accuracy and relevance of evidence, non-classical reasoning approaches are required. Here, we investigate probabilistic epistemic argumentation as a tool for automated reasoning about legal cases. We introduce a general scheme to model legal cases as probabilistic epistemic argumentation problems, explain how evidence can be modeled and sketch how explanations for legal decisions can be generated automatically. Our framework is easily interpretable, can deal with cyclic structures and imprecise probabilities and guarantees polynomial-time probabilistic reasoning in the worst-case.

In moments of digital anxiety I find myself thinking of my father's desk. Dad was a travelling furniture salesman in the 1980s, a job that served him well in the years before globalisation hobbled the Canadian manufacturing sector. He was out on the road a lot, but when he worked from home he sat in his office, a small windowless study dominated by a large teak desk. And yet every day Dad spent hours there, making notes, smoking Craven "A"s, drinking coffee and yakking affably to small-town retailers about shipments of sectional sofas and dinette sets. This is what I find so amazing.

The stretch of summer between E3 and the holiday season is a hard time to be a video game fan. The industry tantalized us with all the wonderful games it's making, but the problem is that most of those new and amazing-sounding games won't come out until at least November. But that just means you've got plenty of time to catch up on all the amazing video games 2019 has already gifted us. Here are the best video games of 2019 so far, including a few that may have slipped under your radar. Come for the incredible visuals, stay for the profound and moving story.

The 18th-century Irish philosopher Bishop George Berkeley concluded that, since all we know of the universe is what our senses convey to us, things in the world exist only to the extent that we perceive them. They have no material reality, but are phenomena in and of our minds, or the mind of God. Samuel Johnson famously countered this philosophy by kicking a large stone and saying, "I refute it thus!" Two hundred years later, while American campuses roiled with protests against the Vietnam War, the philosopher, historian, and physicist Thomas Kuhn met with a grad student at Princeton's legendary Institute for Advanced Study to discuss the student's paper. The professor and student disagreed on some fundamental ideas, and the conversation grew heated.

The debate about the replacement of lawyers by machines is already a cliché cropping up at most industry conferences and seminars, as many lawyers see themselves as threatened by this possibility considering the extraordinary capacities the technology is acquiring. If we focus on the advanced artificial intelligence and document automation tools currently being used in the industry we can say, without fear of contradiction, that jobs at law firms really are going to be destroyed. This phenomenon will even redefine the structure of the biggest firms, the early adopters of these technologies, for which these tools are already almost essential elements for providing service to clients, for example when dealing with due diligence for merger or acquisition processes. These firms will require fewer lawyers to do those jobs. But jobs related to document analysis are not the only ones that will be transformed by the new technology.

Digital services have frequently been in collision -- if not out-and-out conflict -- with the rule of law. But what happens when technologies such as deep learning software and self-executing code are in the driving seat of legal decisions? How can we be sure next-gen'legal tech' systems are not unfairly biased against certain groups or individuals? And what skills will lawyers need to develop to be able to properly assess the quality of the justice flowing from data-driven decisions? While entrepreneurs have been eyeing traditional legal processes for some years now, with a cost-cutting gleam in their eye and the word'streamline' on their lips, this early phase of legal innovation pales in significance beside the transformative potential of AI technologies that are already pushing their algorithmic fingers into legal processes -- and perhaps shifting the line of the law itself in the process.

This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, non-player characters, locks and keys and dialog options. The paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.