1.
TOWARDS RESEARCH-DRIVEN
CURRICULA FOR LAW AND
COMPUTER SCIENCE
Dr AdamWyner
Department of Computing Science
University of Aberdeen
Dr Roy Partain
Law School
University of Aberdeen
BILETA Conference
University of Aberdeen
April 10, 2018

2.
PROBLEM
• How can we develop curricula to equip law school/computer science students
with the skills to understand, critique, use, and develop legal technologies?
• The parameters for new curricula are broad and demanding:
• Various users for legal technologies, each with motivations and requirements
• Abundant legal information and powerful computational tools
• Novel roles to fill in the legal technologies sphere
• AI and Law research literature to design curricula and develop legal technologies.
• Is there a ‘line’ between professional training and training for research?
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3.
CONTEXT
• AI/Technology and Law
• Solicitors Regulation Authority and Bar Standards Board
• Dynamic context for government, judiciary, firms, law schools, public
• Intensive business development
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4.
SCOPE
• Law applied to ComputingTechnologies
• What the law says about: online privacy, autonomous vehicles, intellectual
property,…
• Computing technologies applied to law
• How can we apply computational tools to : case-based reasoning,
information extraction, argumentation, legal process management, contract
construction, …
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5.
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6.
ROLES
• Knowledge engineer – represent, process, and reason with legal knowledge
• Technologist – working with available tools (OPA, contracts, ODR, etc.)
• Process analyst – broker for subprocesses of a legal action
• Project manager – sees a complex process thorough
• Data scientist – mines and interprets data
• Risk manager – identifies and manages risk for contracts and compliance
• Others
(from Susskind, 2017)
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7.
COMPUTER SCIENCE BSC
• Computer programming and principles
• Computer architecture
• Web technology and application
development
• Object-oriented programming
• Mathematics for computing science
• Data management
• Human computer interaction
• Algorithmic problem solving
• Modern programming languages
• Operating systems
• Principles of software engineering
• Languages and computability
• Knowledge-based systems
• Robotics
• Distributed systems and security
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8.
WITHIN A TOPIC AREA - AI
• Problem-solving by search
• Multi-agent systems
• Logic
• Set theory
• Knowledge representation and reasoning
• Perception
• Planning
• Uncertainty – probabilistic,
argumentation
• Learning – observations, statistical,
reinforcement
• Natural language processing and
generation
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9.
COMPUTER SCIENCE CAREERS
• Software application developer
• Computer systems analyst
• Software systems developer
• Web developer
• Network systems administrator
• Database administrator
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10.
LLB
• Criminal law
• Legal system
• Legal method
• Legal theory
• Contract
• Public law and human rights
• Delict and unjustified enrichment
• EU Institutions and law
• The law of property
• Family law
• Succession and trusts
• Evidence
• Commercial and consumer contracts
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Lots of articulation
within a topic area

11.
LAW CAREERS
• Advice worker
• Barrister
• Barrister’s clerk
• Chartered accountant
• Chaterted legal executive
• Civil service
• Company secretary
• Lecturer
• Licensed conveyancer
• Paralegal
• Patent attorney
• Police
• Researcher
• Solicitor
• Stockbroker
• Trading standards officer
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12.
MASH UP
• How to mix and match?
• How to do so without being too superficial in any one (or all) thing(s)?
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13.
LAW WITH COMPUTING SCIENCE
UNIV. OF ABERDEEN
• Year 1: 8 year one law + 2 level one computer science
• Year 2: 7 year two law + 2 level two computer science
• Year 3: 2 year three law + 3 level three computer science
• Year 4: dissertation
https://www.abdn.ac.uk/study/undergraduate/degree-programmes/1146/M1G1/bachelor-of-laws-
with-computing-science/
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14.
LLM LEGALTECH
SWANSEA UNIVERSITY
• AI and law
• Automating legal services
• Computational thinking for lawyers
• Quantitative analysis and big data
• Blockchain/distributed ledger technology
• Rights and accountability in the digital
economy
• Legal services in a digital world
• LegalTech entrepreneurship
• Digital intellectual property
http://www.swansea.ac.uk/postgraduate/taught/coming-soon/#legaltech-llm=is-expanded
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15.
OTHERS
• Python programming with some legal examples
• Georgetown
• Harvard
• Getting at the law or computer science?
• What can be done with this amount of programming?
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16.
PATCH
• Asking students to
• figure out the connections
• envision analyses and implementations
• implement
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17.
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18.
WHAT DO WE GET AT THE LEVELS?
• L1: Knowledge, comprehension, application
• Understand what a tool does and why
• Use/apply a tool
• Critique the strengths and weaknesses of a tool (needs information on possibilities)
• L2: Analysis, synthesis, evaluation
• Analyse given problems, systems, and tools
• Create novel computational tools and systems
• Determine the adequacy or appropriateness of a tool
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19.
LEGALTECH USE (L1) V.
COMPUTATIONAL ANALYSIS (L2)
• Analysing a problem from a computational point of view
• Algorithms – explicit sequence of processes from input to output
• Developing an algorithm leads one to decompose a large, complex
problem/task into smaller, implementable component parts.Then recompose
them into a larger solution.
• Prior to an implementation
• More generic, long-lasting, substantive than any implementation
• May lead to discoveries and a deeper understanding of a problem/issue
• Future proof intellectual skills
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20.
CAN V. OUGHT
• The problem, context, and spectrum of knowledge/skills require that
• while L1 can be done (teaching specific and practical knowledge/skills)
• L2 ought to be done (generic analysis skills for ongoing development)
• Distinction is already in legal and computer science training
• teach principles and topic areas
• not specific practices/techniques/tools
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21.
COLLABORATIONS IN SOFTWARE
ENGINEERING CYCLE
Requirements
analysis
Design
ImplementationEvaluation/testing
Evolution
What are the concepts,
problems, or issues?
How is something practiced?
What/where is the data?
What are the goals?
What is the reasoning?
What are the elements,
relations, and properties?
What are the actions?
What are the outcomes?
What is the algorithm or
logic?
Is the system working as
intended (right output)?
Are there errors/confusions?
How should it be changed?
What should be added?
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22.
LAW FROM A ‘SCIENTIFIC’ ANGLE
• The cycle applies a scientific method to legal information and practice
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23.
DYNAMIC CURRICULA
• Introductory level: programming, logic/math, algorithms, analysis, law, language
• Higher levels:
• project-based – pick a topic/problem to address
• collaborative work in teams – analysis and implementation are often larger
than one person.
• interdisciplinary – work/communicate across disciplines.
• modular – ongoing training/courses are sprints for understanding and skills.
Some ‘trails’ and some independent.
• Computer science practice
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24.
THANKS
• Adam Wyner – azwyner@abdn.ac.uk
• Roy Partain – roy.partain@abdn.ac.uk
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