Course
5895: Artificial Intelligence and Legal Reasoning Seminar
Syllabus: January 1,
2017
Version: 1.0
Class
URL: http://www.lrdc.pitt.edu/Ashley/ailawsyl17.htm
Time
and Place: Spring Semester, 2017:
Wednesdays, 9:00 AM - 11:20 PM, Law Bldg G18
Professor: Kevin D. Ashley, Professor of Law and Intelligent
Systems
Law Bldg,
3900 Forbes Ave., Room 525, (412) 648-1495
Learning Research and Development Center, 3939 O'Hara St., Room 519, (412)
624-7496
email: ashley@pitt.edu
Seminar
description and rationale:
How will the technology of legal practice develop
in the near future, and what challenges and opportunities will these changes
present for young attorneys and technologists? Machine learning applications
and start-ups are revolutionizing e-discovery in litigation. IBM Corp. may soon
apply its ÒJeopardy!Ó game-winning Watson technology to question-answering
and argument-making in law. These and other developments in natural language
processing, argument mining, and automated information extraction offer tools
for designing new process models for delivering legal services, promising
greater efficiency and public accessibility. Legal reasoning, however, does not
change as quickly. How will the new tools accommodate, employ and adapt the
structures of legal knowledge such as arguments from statutes, regulations,
cases, and policies? How, for example, will Watson reason with these legal
sources?
Researchers in Artificial Intelligence (AI) and law
are addressing questions like these as they design computer programs that can
perform legal reasoning or that can solve, or assist attorneys in solving,
legal problems. The researchers address how to represent what a legal rule
means so that a computer program can decide whether it applies to a situation,
how to separate ÒhardÓ from ÒeasyÓ legal issues, and the roles that cases and
values play in interpreting legal rules. Their answers, however, are not
philosophical but scientific; they design computer programs that model the
tasks and conduct experiments to evaluate how well the programs perform.
Increasingly, they integrate their models with the underlying legal texts for
purposes of improving legal information retrieval, prediction and decision-making.
In this seminar, law students and graduate students
from other disciplines will consider these questions after reading excerpts
from comprehensible research papers. The seminar will introduce the
fundamentals of AI to law students and of legal reasoning to graduate students.
Law students will more fully appreciate the techniques and assumptions employed
by attorneys, and by AI and law researchers, to deal with the uncertainties
inherent in legal reasoning. No familiarity with computer programming is
required. Students will be asked to prepare short summaries (1-2 pages)
of selected readings and a seminar paper. In lieu of a seminar paper, students
with approval of the instructor may design and build an AI-related program for
a legal application using new tools that do not require programming
experience. (Such a program will not satisfy the legal writing
requirement.) Graduate students are invited to propose relevant seminar paper
topics close to their research interests.
Seminar
topics overview:
This seminar provides an overview of work in AI and
Law, and then focuses on very recent developments in information extraction
from text for purposes of computerized question-answering
and generation of arguments. These developments promise to break the knowledge
acquisition bottleneck that has long hampered progress in fielding practical
applications in AI and Law. They raise an interesting question, however. Now
that we can soon extract information automatically from legal texts, what can
programs do with it? And, exactly what kind of information should be extracted?
Certainly, the extracted information will be used to improve legal information
retrieval, helping to point legal professionals more quickly to relevant
information, but can more be done? Can computers reason with the legal
information extracted from texts? That has been the goal of much of the work in
AI & Law: developing computational models of reasoning with legal
information extracted from legal texts but extracted primarily manually, at least up to now.
Thus, in the first weeks of the seminar, we will
focus in some detail on the nature of legal decision-making and some rule-based
or case-based approaches to modeling it computationally. We briefly study an AI
model of the kind of issue spotting law students perform in taking exams and
then focus on some formal logical models of statutory reasoning and various
lessons learned from these efforts. The seminar then takes a more detailed look
at reasoning with legal cases or precedents, an important phenomenon in common
law jurisdictions. We will compare and contrast a number of computational
models of case-based legal reasoning in terms of how the legal information is
represented and the extent to which they take into account underlying legal
values. In particular we will address the following questions:
á
What is the
process of reasoning with precedents in common law legal practice?
á
What aspects
of this process do the computational models of case-based legal reasoning
address? What is missing?
More recent work on computational models of
argumentation unifies reasoning logically with legal rules and analogically
with legal precedents. The models generate legal arguments represented
diagrammatically for purposes of teaching or public discussion of legal issues.
Finally, we turn to some practical applications of AI-supported tools for legal
information retrieval from text, e-discovery, corporate compliance, network
analysis of legal institutions, and helping judges and legal professionals with
document drafting, sentencing, and other aspects of legal decision-making.
TWEN Course Website:
There
is a TWEN website for this course, Artificial Intelligence and Legal Reasoning
Seminar, at lawschool.westlaw.com.
In order to access the site, you will need to use your Westlaw password and a
course password to be distributed via email. Beside the syllabus, the website
will contain materials for distribution and a discussion board.
Materials:
The readings
are primarily from Artificial
Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age,
a new book by Kevin D. Ashley, to be published in 2017 by Cambridge University
Press. The readings will be
distributed electronically via the TWEN course website. This material is an uncorrected final draft of the book. It is for personal use only. Seminar
participants should not copy or distribute
it.
Evaluation
and Requirements:
Attendance will be taken on a regular basis
throughout the semester. The main requirements are (1) a seminar paper or
project and short presentation, (2) weekly short write-ups on selected
readings, (3) classroom participation, and (4) participation in the
collaborative design problem described below. Seminar grades will be based on a
studentÕs seminar paper final draft or seminar project, oral presentation,
classroom participation, one-page reading write-ups, and good faith
participation in the collaborative design problem.
Seminar
papers (or projects) and short presentations:
Students
will be asked to write a fifteen-page (minimum) paper on a topic of their
choice approved by the instructor. A non-exhaustive listing of possible paper
topics may be found at sampletopics2017.htm.
Students should contact the instructor early in the term to discuss appropriate
paper topics. This is especially true for those who intend the paper to satisfy
their law school writing requirement. Whatever paper topic a student chooses, a
student should plan to develop a thesis and include an extended specific
example or examples to illustrate his/her points. Graduate students are invited
to propose paper topics connecting the seminar material to their own research
interests in AI or computer science.
Legal application in lieu of
seminar paper: In lieu of a seminar paper, students with approval of the
instructor may design and build an AI-related program
or module for a legal application. For example, students may use publicly
available machine learning programs or resources from the IBM Watson Developer
Cloud. (Such a program will not satisfy the legal writing requirement.)
Students
will be asked to make an oral presentation of their paper or project at the end
of the semester.
Classroom
Participation:
In order to stimulate classroom discussion and
foster understanding of the readings, for each session, each student should
prepare a one-page critique of each sessionÕs readings. Generally, each
sessionÕs readings comprise one chapter of the Artificial Intelligence and Legal Analytics
book. These one-page critiques should be submitted electronically to the
instructor by midnight before each seminar session. Each student should select
some approach described
in the dayÕs readings that he/she find interesting and write:
(a) A very brief description of the approach,
and short descriptions of:
(b) The strengths of the approach,
(c) The weaknesses of the approach, and
(d) The reasons why the student finds the
approach of interest. Students should try to relate it to Cognitive Computing (to be defined in the first
class) and some
specific legal project, paper, or educational experience in which the student
has been involved.
The instructor will assign particular students
responsibility for leading discussions of particular readings at the next
class.
Collaborative
Design Problem:
New text analytic techniques promise to revolutionize
legal information retrieval, but the machine learning on which they depend
requires sets of training instances comprising manually annotated legal cases
and statutes. ÒAnnotatingÓ means marking-up the texts
of case decisions or statutes to identify instances of semantic types of
information that are both important for conceptual legal information retrieval
and pedagogically relevant. In order to support this need for manual annotation
of legal texts, it is possible that law students could perform these
annotations as part of their studies. As a by-product, they would produce the
semantically annotated legal texts with which machine learning programs could
be taught to automatically perform similar annotation of new texts.
As an extended collaborative design problem, the
seminar will devote time each week to brainstorming and designing a pedagogical
intervention involving annotating cases that could be applied in a legal
course. This will involve demonstrations of programs and approaches, including:
-
An interactive
tool to select texts relevant for statutory or trade secret case analysis.
-
The WebAnno annotation environment.
-
The Value
Judgment-based Argumentative Prediction (VJAP) Model, possibly analyzing
student-annotated trade secret cases.
-
Automated
summarization of trade secret cases.
Seminar students will use the WebAnno
tool to annotate trade secret cases in terms of sentence roles in legal
argument and legal factors (to be defined).
A more detailed description of the collaborative
design problem will be found here.
Schedule
of Reading
I. Introducing AI & Law and its Role in
Future Legal Practice
Date: January 11
Readings: Chapter 1 of Artificial Intelligence and Legal Analytics (available on TWEN site) and Collaborative
design problem description
á
Discussion of possible paper topics.
á
Discussion of collaborative design problem re pedagogical
uses of case annotation
II. Modeling
Statutory Reasoning
Date: January 18
Readings: Chapter 2, Artificial
Intelligence and Legal Analytics
III.
Modeling Case-Based Legal Reasoning
Date: January 25
Readings: Chapter 3, Artificial Intelligence
and Legal Analytics
IV. Models
for Predicting Legal Outcomes
Date: February 01
Readings: Chapter 4, Artificial Intelligence and Legal Analytics
V.
Computational Models of Legal Argument
Date: February 08
Readings: Chapter 5, Artificial Intelligence
and Legal Analytics
VI.
Representing Legal Concepts in Ontologies and Type Systems
Date: February 15
Readings: Chapter 6, Artificial Intelligence
and Legal Analytics
VII. Making
Legal Information Retrieval Smarter
Date: February 22
Readings: Chapter 7, Artificial Intelligence
and Legal Analytics
VIII. Machine Learning with Legal Texts
Date: February 29
Readings: Chapter 8, Artificial Intelligence
and Legal Analytics
IX.
Extracting Information from Statutory and Regulatory Texts
Date: March 1
Readings: Chapter 9, Artificial Intelligence
and Legal Analytics
[Note: No
class on March 08, Spring Recess]
X.
Extracting Argument-Related Information from Legal Case Texts
Date: March 15
Readings: Chapter 10, Artificial Intelligence and
Legal Analytics
XI.
Conceptual Legal Information Retrieval for Cognitive Computing
Date: March 22
Readings: Chapter 11, Artificial Intelligence
and Legal Analytics
XII.
Cognitive Computing Legal Apps
Date: March 29
Readings: Chapter 12, Artificial Intelligence
and Legal Analytics
XIII. Next
Steps toward Realizing Cognitive Computing Legal Apps
Date: April 5
XIV.
Collaborative Design Problem Wrap-up
Date: April 12
XV. Student
Short Presentations of Seminar Papers
Date: April 19
á
StudentsÕ give short presentations of their papers or
projects.