This is a Zettelkasten for the reading list of CCLAW@SMU. The aim of the research programme is to improve legal drafting and automate the parts that are well suited for automation.
Contributions to this zettelkasten are welcome. This is not an official document, it’s just learning notes of individual persons who are learning about the different aspects of computational law. So it is very likely that some of the zettels contain mistakes. (If you notice one, consider correcting it or leaving a note!)
Problems in traditional legal drafting
(where “traditional” = humans draft in natural language.)
Natural language is imprecise
Humans and natural language tolerate lot of things that computers wouldn’t, such as:
- Incomplete information
- Semantic ambiguity, vagueness
- Syntactic ambiguity
- Fuzzy definitions; everyday speech vs. logical definition
On the other hand, real-life situations are often vague and ambiguous. For those situations, we need the flexibility of human-written documents.
But there are a lot of situations where that flexibility isn’t as crucial, and we want to automate that area. It’s like leaving poetry for professional translators and automating instruction manual translation.
Plain text is a suboptimal format
- Can’t be checked for inconsistencies (except by humans who aren’t good at it)
- Can’t keep track of the overall state. If one thing changes, the rest needs to be updated manually.
Approaches to automate/improve
If you’re unsure where to start, start from basics.
You’re welcome to revise, add to, split, merge etc. any entry in this zettelkasten. But these ones with the tag
TODO are particularly unfinished.