Introduction: Cybersecurity in Pittsburgh

This article provides a brief introduction to cybersecurity issues in the Pittsburgh region and introduces the student article series

Teaching Law and Digital Age Legal Practice with an AI and Law Seminar

This article provides a guide and examples for using a seminar on Artificial Intelligence (AI) and Law to teach lessons about legal reasoning and about legal practice in the digital age. Artificial Intelligence and Law is a subfield of AI/ computer science research that focuses on computationally modeling legal reasoning. In at least a few law schools, the AI and Law seminar has regularly taught students fundamental issues about law and legal reasoning by focusing them on the problems these issues pose for scientists attempting to computationally model legal reasoning. AI and Law researchers have designed programs to reason with legal rules, apply legal precedents, predict case outcomes, argue like a legal advocate and visualize legal arguments. The article illustrates some of the pedagogically important lessons that they have learned in the process. As the technology of legal practice catches up with the aspirations of AI and Law researchers, the AI and Law seminar can play a new role in legal education. With advances in such areas as e-discovery, legal information retrieval (IR), and semantic processing of web-based information for electronic contracting, the chances are increasing that, in their legal practices, law students will use, and even depend on, systems that employ AI techniques. As explained in the Article, an AI and Law seminar invites students to think about processes of legal reasoning and legal practice and about how those processes employ information. It teaches how the new digital documents technologies work, what they can and cannot do, how to measure performance, how to evaluate claims about the technologies, and how to be savvy consumers and users of the technologies

Wildland Recreation Disturbance: Broad‐Scale Spatial Analysis and Management

Wildland recreation that does not involve animal harvests (non‐consumptive recreation) often influences various components of natural systems, including soils, water, air, soundscapes, vegetation, and wildlife. The effects of non‐consumptive recreation on wildlife have typically been assessed at spatial scales that are not only much smaller than the overall distributions of this disturbance but also much smaller than the areas that species use during a season or year. This disparity in scales has prevented effective assessment and management of broad‐scale recreation disturbance for many species, especially wildlife. We applied three software systems (ArcGIS, FRAGSTATS, and Conefor) to demonstrate how metrics commonly measured by landscape ecologists can be used to quantify broad‐scale patterns of non‐consumptive recreation. Analysts can employ such metrics to develop predictive models of how recreation disturbance – by itself and in additive or interactive combinations with other landscape characteristics – may affect wildlife responses across large areas. In turn, these models can inform decision making in broad‐scale recreation management

Adaptive Rückmeldungen im intelligenten Tutorensystem LARGO

The Intelligent Tutoring System LARGO is designed to help law students learn argumentation skills. The approach implemented in LARGO uses transcripts of oral arguments as learning resources: Students annotate them and create graphical representations of the argument flow. The system encourages students to reflect upon arguments proposed by the attorneys and helps students detect possible weaknesses in their analysis of the dispute. Technically, graph grammar and collaborative filtering algorithms are employed to detect these weaknesses. This article describes how “usage contexts” are determined and used to create adaptive feedback in LARGO. On the basis of a controlled study with the system that took place with law students at the University of Pittsburgh, we discuss to what extent the automatically calculated usage contexts can predict student’s learning gains

Using Event Progression to Enhance Purposive Argumentation in the Value Judgment Formalism

ABSTRACT This paper expands on the previously published value judgment formalism. The representation of situations is enhanced by introducing event progressions similar to actions in general AI planning. Using event progressions, situations can be assessed as to what facts they contain as well as what facts may ensue with some likelihood, thereby opening up a situation space. Purposive legal argumentation can be modeled using propositions and rules controlling the likelihoods of value-laden consequences. The paper expands the formalism to cover event progressions and illustrates the functionality using an example based on Young v. Hitchens

Toward Adding Knowledge to Learning Algorithms for Indexing Legal Cases

Abstract Case-based reasoning systems have shown great protnise for legal argumentation, but their development and wider availability are still slowed by the cost of manually representing cases. In this paper, we present our recent progress toward automatically indexing legal opinion texts for a CBR system. Our system SMILE uses a classijication-based approach tojnd abstract fact situations in legal texts. To reduce the cotnple.rity irzherent in legal texts, we take the individud sentences from a marked-up collection of case sumtwries as examples. We illustrate how integrating a legal thesaurus a& linguistic information with a machine learning algorithm can help to overcome the diSJiculties creuted by legal language. The paper discusses results from a preliminary experiment with a decision tree learning algorithm. Experiments indicate that learning on the basis of sentences, rather than full documents, is effective. They also confirm that adding a legal thesaurus to the learning algorithm leads to improved pet$ormance for some, but not all. indexing concepts

Explaining Legal Concepts with Augmented Large Language Models (GPT-4)

Interpreting the meaning of legal open-textured terms is a key task of legal professionals. An important source for this interpretation is how the term was applied in previous court cases. In this paper, we evaluate the performance of GPT-4 in generating factually accurate, clear and relevant explanations of terms in legislation. We compare the performance of a baseline setup, where GPT-4 is directly asked to explain a legal term, to an augmented approach, where a legal information retrieval module is used to provide relevant context to the model, in the form of sentences from case law. We found that the direct application of GPT-4 yields explanations that appear to be of very high quality on their surface. However, detailed analysis uncovered limitations in terms of the factual accuracy of the explanations. Further, we found that the augmentation leads to improved quality, and appears to eliminate the issue of hallucination, where models invent incorrect statements. These findings open the door to the building of systems that can autonomously retrieve relevant sentences from case law and condense them into a useful explanation for legal scholars, educators or practicing lawyers alike

Effect of Inductive Coil Geometry and Current Sheet Trajectory of a Conical Theta Pinch Pulsed Inductive Plasma Accelerator

Results are presented demonstrating the e ect of inductive coil geometry and current sheet trajectory on the exhaust velocity of propellant in conical theta pinch pulsed induc- tive plasma accelerators. The electromagnetic coupling between the inductive coil of the accelerator and a plasma current sheet is simulated, substituting a conical copper frustum for the plasma. The variation of system inductance as a function of plasma position is obtained by displacing the simulated current sheet from the coil while measuring the total inductance of the coil. Four coils of differing geometries were employed, and the total inductance of each coil was measured as a function of the axial displacement of two sep- arate copper frusta both having the same cone angle and length as the coil but with one compressed to a smaller size relative to the coil. The measured relationship between total coil inductance and current sheet position closes a dynamical circuit model that is used to calculate the resulting current sheet velocity for various coil and current sheet con gura- tions. The results of this model, which neglects the pinching contribution to thrust, radial propellant con nement, and plume divergence, indicate that in a conical theta pinch ge- ometry current sheet pinching is detrimental to thruster performance, reducing the kinetic energy of the exhausting propellant by up to 50% (at the upper bound for the parameter range of the study). The decrease in exhaust velocity was larger for coils and simulated current sheets of smaller half cone angles. An upper bound for the pinching contribution to thrust is estimated for typical operating parameters. Measurements of coil inductance for three di erent current sheet pinching conditions are used to estimate the magnetic pressure as a function of current sheet radial compression. The gas-dynamic contribution to axial acceleration is also estimated and shown to not compensate for the decrease in axial electromagnetic acceleration that accompanies the radial compression of the plasma in conical theta pinches