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TLS0070 Introduction to Legal Technology
Lecture 3 Artificial intelligence and law: the 21st century University of Turku Law School 2015-01-27 Anna Ronkainen @ronkaine [email protected]
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Overall claim: Law is ~20 years behind other fields in intelligent tech adoption - nearby point of reference: language
technology - things originally considered AI don’t seem
all that impressive anymore (only annoying when not functioning properly): - spelling and grammar checking - speech recognition and generation - machine translation - ...
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Why? - lawyers are conservative (but that’s too easy
an explanation) - lack of practically relevant research? - lack of commercial incentives - jurisdictional etc fragmentation means the
incentives are even smaller (but it’s the same for languages)
- law is HARD (but then you should just start with the low-hanging fruits)
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How technologies change (or not): an example
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What I worked on through much of law school... AnswerWizard/IntelliSearch, an intelligent tool for providing answers from on-line help files to questions posed in natural language, introduced in Microsoft Office 95:
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But the next version (Office 97) might be more recognizable...
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But the next version (Office 97–) might be more recognizable...
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The basic tech was originally developed at the Stanford Research Institute (SRI)...
... and 10 years later, the same project gave us
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The basic tech was originally developed at the Stanford Research Institute (SRI)...
... and 10 years later, the same project gave us Siri:
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Another example: Watson: the Jeopardy-winning computer by IBM https://www.youtube.com/watch?v=lI-M7O_bRNg A different application https://www.youtube.com/watch?v=7g59PJxbGhY
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DeepQA (Watson) high-level architecture
http://researcher.watson.ibm.com/researcher/view_group_subpage.php?id=2159
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Watson merging and ranking algorithm
http://brenocon.com/watson_special_issue/14%20a%20framework%20for%20merging%20and%20ranking%20answers.pdf
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...and we get
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Modern approaches to legal AI: some examples
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Putting it all together: From raw materials to Getting Things Done™ - Semantic Finlex: legislation as linked open
data - self-organized law systematics - recommender engine for law - INDIGO: intelligent backoffice processing for
public administration ...and plenty others (a task-based overview coming up at lectures 5–7)
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Semantic Finlex - project carried out at Aalto U by Frosterus,
Tuominen, Hyvönen, funded by Tekes - Finnish legislation and case law as linked
open data - uses an ontology for legal source metadata
(which can be used to link them) - http://www.ldf.fi/dataset/finlex
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(Frosterus et al 2014)
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(Frosterus et al 2014)
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Pros and cons - these kinds of resources are mandatory as
building blocks for more advanced things - it is available for Free™ - semantic enhancement only covers metadata
(not legal concepts, yet anyway) - based on 2012 legislation, no updates - only discovers explicit references
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Systematizing Estonian laws through self-organization - project carried out at Tallinn U of Tech by
Täks et al - legal acts modelled as term vectors (based
on occurrences of individual words in each document) which are used to generate a self-organizing map (SOM, Kohonen)
- provides a 2-dimensional map of hypothetical (and also actual) relationships between statutes
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(Täks & Lohk 2010)
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(Täks & Lohk 2010)
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Recommender engine for legal sources - project carried out at the Leibniz Centre at U of
Amsterdam by Winkels et al - uses networks of references (legislation ->
legislation, case law -> legislation) to find all documents matching the current document within a given horizon
- uses network topology based metrics to find the best matches (but plenty of other metrics to choose from)
- currently only prototype; in production could also learn from behavioural data (just like your favourite online store!)
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Intelligent case management platform: INDiGO - project carried out for the Dutch
Immigration and Naturalization Service (IND) by Ordina, Accenture, Be Informed
- replaced an earlier paper-based administrative procedure
- intelligent decision support based on decision trees and checklists
- rules modelled in the system using a proprietary language
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Semantic models in INDiGO - core taxonomies - regulations - online front office (UI) - catalog (index)
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http://vimeo.com/43187024
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Questions?