Metadata-Version: 2.1
Name: obiter
Version: 0.0.1
Summary: Computational research tools for lawyers
Home-page: https://github.com/simon-lawyer/obiter
Author: Simon Wallace
Author-email: simonwallace@osgoode.yorku.ca
License: Apache Software License 2.0
Keywords: legal lawyer research jupyter notebook python
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fastcore
Requires-Dist: pandas
Requires-Dist: requests
Requires-Dist: seaborn
Provides-Extra: dev

Obiter.AI
================

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Computational methods and artificial intelligence will transform the
study and practice of law by exponentially expanding the reach of
empirical enquiries.

<div class="column-margin">

<figure>
<img src="images/computer_calls.jpg"
data-fig-alt="&quot;Computer calls a database,&quot; AI artist (2022)"
alt="“A computer calls a database,” AI artist (2022)" />
<figcaption aria-hidden="true">“A computer calls a database,” AI artist
(2022)</figcaption>
</figure>

</div>

Over the past few decades, the volume of legal data increased
exponentially. In Canada, an average size tribunal will issue tens of
millions of words each year. Thousands of hours of proceedings will be
recorded. Trillions of words will be filed as evidence.

Making sense of, and understanding this data, is a pressing challenge
for scholars and lawyers. Is the law consistent? Do different
adjudicators reach similiar conclusions when presented with similiar
facts? What types of disputes are people bringing to decision makers?
How are those disputes resolved?

Answering these questions at scale exceeds human capacities. Consider
this example. In 2021, the [Ontario Workplace Safety and Insurance
Appeals Tribunal](https://www.wsiat.on.ca/en/home/announcements.html),
issued [2,053 written
decisions](https://www.canlii.org/en/on/onwsiat/nav/date/2021/). If each
decision averages 2,500 words in length, the tribunal outputted
5,132,500 words—the equivalent of 9 editions of *War and Peace*.
Practically, this sheer quantity of data means that the jurisprudence
regarding workers, disability, and compensation cannot be
comprehensively grasped or synthesized by researchers. Who could ever
read so much?

But computers are not so limited. Recent advances in artificial
intelligence and machine learning have significantly expanded machines’
ability to understand, organize, and sythesize complex data. Computers
can now credibly answer complex questions about documents, detect
patterns, and reason with facts.

Lawyers, law students, and researchers should understand how these
methods can be leveraged for research at scale. The goal of
**Obiter.AI** is to build out a suite of open source and accessible
computational tools to facilitate computational research of Canadian
law.
