Zvenyach: Coding for Lawyers

V. David Zvenyach has released a book entitled Coding for Lawyers.

The book currently covers regular expressions, Markdown, HTML, data types, using arrays, and coding in Python.

The GitHub repository for the book is at: https://github.com/vzvenyach/codingforlawyers

Here is a description, from the FAQ:

[...] It’s true. Lawyers can code. In fact, if you’re a lawyer, the truth is that it’s easier than you think. I am a lawyer, and a coder. In the course of two years, I have gone from knowing essentially nothing to being a decent coder in several languages. This book is intended to drastically shorten that time for others who, like me, decide that they want to learn to code. [...] At the moment, I am still making many decisions about this project, and I want your feedback. Is it worth it? Are any lawyers actually interested? Are the chapters too dense? Too easy? Are there topics that you definitely want covered? A great way to help would be to send me an email at [the email address listed in the FAQ] and let me know what you think. An even better way is to submit an issue on GitHub or submit a pull request. [...]

For more details, please see the book or the FAQ.

HT @compleatang

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Ramakrishna and Paschke: Semi-Automated Vocabulary Building for Structured Legal English

Shashishekar Ramakrishna and Adrian Paschke presented a paper entitled Semi-Automated Vocabulary Building for Structured Legal English, at RuleML 2014: International Web Rule Symposium, held 18-20 August 2014 in Prague.

Here is the abstract of the paper:

Structured English has been applied as computational independent language for defining business vocabularies and business rules, e.g., in the context of OMG’s Semantics and Business Vocabulary Representation (SBVR). It allows non-technical domain experts to engineer knowledge in natural language, but with an underlying semi-formal semantics which eases the automation of machine transformation into formal knowledge representations and logic-based machine interpretation. We adapt this approach to the legal domain in order to support legal domain experts in their task to build legal vocabularies and legal rules in Structured English from legal texts. In this paper we contribute with a semi-automated vocabulary and rule development process which is supported by automated suggestions of legal concepts computed by a semantic legal text analysis. We implement a proof-of-concept in the KR4IPLaw tool, which enables legal domain experts to represent their knowledge in Structured English. We evaluate the proposed approach on the basis of use cases in the domain of IP and patent law.

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Smith et al.: Legal Responsibility for the Acts of Others: A Logical Analysis

Clara Smith, Erica Calardo, Antonino Rotolo, and Giovanni Sartor presented a paper entitled Legal Responsibility for the Acts of Others: A Logical Analysis, at RuleML 2014: International Web Rule Symposium, held 18-20 August 2014 in Prague.

Here is the abstract of the paper:

This paper offers a logical analysis of two cases where legal responsibility may emerge for the acts of others: (a) reflex responsibility, and (b) responsibility in the negotiorum gestio doctrine. The current contribution works within a fresh multi-modal system where the new operators are introduced for denoting intentions and actions in the interest of other agents, and the objectively ideal sets of actions for agents.

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Zvenyach: The New Codification Movement

V. David Zvenyach gave a presentation entitled The New Codification Movement, 11 September 2014 for the MIT Legal Physics Research Team, led by Dazza Greenwood.

Here are selected notes about the presentation, from the hackpad for the event, written by Dazza and William P. Li:

Codification: put all the laws in one place [...]

within the next two years, “the new codification movement” — laws will be codified in digital format

What is required? Three Stages:

1. UELMA (Uniform Electronic Legal Material Act)

  • Authentication: Provenance, ensuring that the document is authentic/has not been tampered with
  • Preserved: documents need to be kept/preserved (need a solution for primary legal materials)
  • Accessible: no arbitrary walls between public and the law

2. Law as Data

  • Data Model: what happens when a law is amended, then the original law is repealed?
  • Data Presentation: how should data be made available? bulk downloads, APIs, viewing in different formats

3. Legal Data Science

Once you have the law in a machine-readable form, what can you do with it?

  • Integrated Development Environment (IDE) for Law [...]
  • Version Control [...]
  • Reality Mining [...]
  • Algorithmic Law [...]

Call to Action:

[...]

For more details, please see the complete presentation.

HT @Colarusso

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Ramakrishna and Paschke: A Process for Knowledge Transformation and Knowledge Representation of Patent Law

Shashishekar Ramakrishna and Adrian Paschke presented a paper entitled A Process for Knowledge Transformation and Knowledge Representation of Patent Law, at RuleML 2014: International Web Rule Symposium, held 18-20 August 2014 in Prague.

Here is the abstract of the paper:

Automated support to model and reason based on such modeled legal norms using expert systems, for its use scenarios such as court-filings or argumentation has increasingly become a subject of interest in last few decades. The core problem in all such automation is removing the vagueness embedded within legal texts/sections and this vagueness is due to the pragmatics involved. As of today, we believe, it is impossible for a system to handle any such problems dealing with legal pragmatics. This work proposes a process which acts a bridge between a legal practitioner and a knowledge modeler wherein, a legal practitioner provides the legal information pertaining to a section in a simpler form as required by the modeler. We also propose several knowledge representation formats to represent the information at each layer of the proposed process. Additionally during the course of the paper, we propose a mapping scheme from legal norms in natural language format to Controlled Natural Language (CNL) format and finally to a platform independent rule representation format.

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