Lectures

Blockchain Technology and Smart Contracts for Law: Theory, Practice, and Implications – Silvia Bartolucci

Abstract:

This two-part lecture series explores how blockchain technology and smart contracts are reshaping legal systems, combining foundational concepts with practical applications and case studies. In the first session, participants will learn the mechanics of blockchain and smart contracts, including their key features like immutability, transparency, and automation. Real-world examples such as Ethereum-based escrow agreements and tokenized asset contracts will demonstrate how these technologies work in practice.

The second session examines legal applications, focusing on case studies exploring blockchain’s use cases in the legal sector. These examples show how the technology is being used for automating contracts, managing intellectual property, resolving disputes, and enhancing compliance. Challenges such as scalability, legal enforceability, and ethical considerations will also be discussed.

This series provides participants with practical knowledge and tools to understand and apply blockchain and smart contracts in legal contexts, to address the complexities of modern legal systems with innovative solutions.

Legal Networks: From Principles to Practice – Corinna Coupette

Abstract:

This course will cover the fundamentals of legal network science, from modeling legal networks to analyzing them and communicating the results. Guided by real-world case studies, we will devote particular attention to questions of research design, such as how to ensure construct validity in network measurement and how to deal with diverse “researcher degrees of freedom”. Overall, the course will equip participants with the essential tools and best practices needed to conduct legal network research that can contribute to our collective knowledge and advance the state of the art.

How to model complex legal systems with (appropriately) complex models – Eddie Lee

Abstract and Plan:

The aim is to provide participants with a foundation in the concepts and tools necessary to understand and appreciate applications of statistical physics models of complex systems, particularly in the context of legal systems such as the US Supreme Court.

Lecture 1 (1.5 hours) – Introduction to Probability, Information Theory, and Complex Systems

We will lay the foundation with basic concepts in probability theory and information theory, which are essential for understanding statistical physics modeling. We will also provide an overview of complex systems, highlighting their key characteristics and relevance to various domains, including legal systems.

Complex systems: key characteristics and examples

1. Emergent properties, non-linearity, and self-organization
2. Computational principles in biological and social systems

Basic concepts in probability theory

1. Random variables, probability distributions, and moments
2. Joint, marginal, and conditional probabilities
3. Bayes’ theorem and its applications

Information theory fundamentals

1. Entropy, joint entropy, and conditional entropy
2. Kullback-Leibler divergence and mutual information

Lecture 2 (1.5 hours) – Modeling Interactions: Ising Model and Maximum Entropy Principle

We will focus on the Ising model and the maximum entropy principle. By explaining the historical context, physical interpretation, and mathematical formulation of these concepts, students will gain a deeper understanding of how interactions can be modeled and how the maximum entropy principle justifies the use of the Ising model.

Introduction to the Ising model

1. Historical context and physical interpretation
2. Hamiltonian, spin configurations, and interaction terms

Maximum entropy principle

1. Justification and philosophical implications
2. Deriving the Ising model from maximum entropy

Hamiltonians, free energies, and partition functions

1. Defining and interpreting these key concepts
2. Their roles in statistical physics modeling

Lecture 3 (1.5 hours) Title: Solving Maximum Entropy Models and Applications to Legal Systems

Lecture 3 delves into the practical aspects of solving maximum entropy models and applying them to legal systems. By covering various techniques and presenting case studies from your own research, students will learn how to implement these models, interpret the results, and draw meaningful insights about the complexity of legal systems.

Techniques for solving maximum entropy models

1. Exact enumeration for small systems
2. Monte Carlo methods for larger systems
3. Mean-field approximations and variational methods

Numerical applications and case studies

1. Modeling voting patterns in the US Supreme Court
2. Identifying influential factors and predicting outcomes

Interpreting results and drawing insights

1. Quantifying the complexity of legal systems
2. Implications for understanding and reforming legal processes

Future directions

1. Restricted Boltzmann machines, Potts models, and symmetries

Measuring viability of legal systems when aided computationally – how does one know if a particular tool will ‘work’ in a given system – Cari Hyde-Vaamonde

Participants will explore various methodologies for testing the viability of a system in a legal context. Drawing from recent empirical research we see how we can measure the response of stakeholders to innovative tools and how terms such as “AI Audit” can be operationalised in a legal context. Further, visualisation techniques can transform complex viability metrics into accessible insights for different stakeholder groups. Through hands-on examples from recent projects, participants will explore both quantitative data visualization and qualitative visual storytelling methods that effectively communicate system performance to legal practitioners, policymakers, and the public.

Dirk Hartung: 
Data Science in the Legal Profession – From Theory to Practice

This course introduces students to the fundamental concepts and practical applications of data science in the legal profession. From theoretical foundations to hands-on case studies, we will explore how data-driven methods—particularly natural language processing (NLP)—are transforming legal practice, research, and policy-making. Participants will gain an understanding of the legal, societal, and business contexts in which data science is applied, including professional ethics, regulatory constraints, and the economic realities of legal services. Through case studies, students will examine how data science is used in law firms, in-house legal departments, and courts to analyze legal texts, predict case outcomes, and optimize legal decision-making. By the end of the course, participants will be equipped with essential tools and insights to bridge the gap between technical concepts, prototypes, and real-world legal practice.