Computational research tools for lawyers
Project description
Obiter.Ai
Computational methods and artificial intelligence will transform the study and practice of law by significantly expanding the reach of empirical enquiries.
The volume of legal data increased exponentially over the past decades. 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, issued 2,053 written decisions. 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. The volume 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.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file obiter-0.0.4.tar.gz
.
File metadata
- Download URL: obiter-0.0.4.tar.gz
- Upload date:
- Size: 11.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5cbe9d4366106b0289796edb552ad869dbdfbd9879a1f3cb8b947a144d8912f4 |
|
MD5 | 40003d3f460dc7b5f84b5d3092c497e1 |
|
BLAKE2b-256 | ad2652a375d1c8a4333c17992e2fa2c719387056f13a117abf5cb2eb14837478 |
File details
Details for the file obiter-0.0.4-py3-none-any.whl
.
File metadata
- Download URL: obiter-0.0.4-py3-none-any.whl
- Upload date:
- Size: 10.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5c7958e884c162b063fc085d89bf539a99ae1f9faea586ae3bc885969a42a022 |
|
MD5 | 863926a00b16bf6edbdf53abdc0102c6 |
|
BLAKE2b-256 | b95f1c589463e3c1abba402bd5c89e7af07199dd7babe4752466e8d459936f44 |