Previously: what is law? - part 10
Gliding gracefully
over all the challenges alluded to earlier with respect to
extracting the text level meaning out of the corpus of Law at time T,
we now turn to thinking about how it is actually interpreted and
utilized by practitioners.
To do that, we will continue with our
useful invention of an infinitely patient person who has somehow
found all of the
primary corpus and read it all from the master
sources, internalized it, and can now answer our questions about it
and feed it back to us on demand.
The first order of
business is where to start reading? There are two immediate issues
here.
Firstly, the corpus is not chronologically accretive. That is,
there is no "start date" to the corpus we can work from,
even if, in terms of historical events, a foundation date for a state
can be identified. The reasons for this have already been discussed.
Laws get modified. Laws get repealed. Caselaw gets added. Caselaw
gets repealed. New laws get added. I think of it like a vast stormy
ocean, constantly ebbing and flowing, constantly adding new content
(rainfall, rivers) and constantly loosing content (evaporation) - in
an endless cycle. It has no "start point" per se.
In the absence of an
obvious start point, some of you
may be thinking "the index",
which brings us to the second issue. There is no index!
There is no
master taxonomy that classifies everything into a nice tidy
hierarchy. There are some excellent indexes/taxonomies in the
secondary corpus produced by legal publishers, but not in the primary
corpus.
Why so? Well, if you
remember back to the Unbounded Opinion Requirement mentioned
previously, creating an index/taxonomy is, necessarily, the creation
of an opinion on the
"about-ness" of a text in the corpus.
This is something the corpus of law stays
really quite vague about -
on purpose - in order to leave room for interpretation
of the
circumstances and facts about any individual legal question. Just
because
a law was originally passed to do with electricity usage in
phone lines, does not mean it is not applicable to computer hacking
legislation. Just because a law was passed relating to manufacturing
processes does not mean it has no relevance to ripening bananas.
(Two examples based on real world situations, I have come across by
the way.)
So, we have a vast,
constantly changing, constantly growing corpus. So big it is
literally humanly impossible to read, regardless of the size of your
legal team, and there are no finding aids in the primary corpus to
help us navigate our way through it....
...Well actually,
there is one and it is an incredibly powerful finding aid. The corpus
of
legal materials is woven together by an amazingly intricate web
of citations. Laws
invariably cite other laws. Regulations cite
laws. Regulations cite regulations. Caselaw
cites law and
regulations and other caselaw....creating a layer that computer
people would call a network graph[1]. Understanding the network graph
is key to understanding how practitioners navigate the corpus of law.
The don't go page-by-page, or
date-by-date, they go
citation-by-citation.
The usefulness of
this citation network in law cannot be overstated. The citation
network helps practitioners to find related materials, acting as a
human-generated
recommender algorithm for practitioners. The
citation networks not only establish
related-ness, they also
establish meaning, especially in the caselaw corpus. We
talked
earlier about the open-textured nature of the legal corpus. It is not
big
on black an white definitions of things. Everything related to
meaning is fluid on
purpose. The closest thing in law to true
meaning is arguably established in the
caselaw. In a sense, the
caselaw is the only source of information on meaning that really matters
because at the end of the day, it does not matter what you or I or
anyone else might
think a part of the corpus means. What really
matters is what the courts say it means.
Caselaw is the place you go
to find that out.
"But", I
hear you say, "graphs do not necessarily have a start point
either!". True.
But this is where one of the real skills of a lawyer
manifests itself. Legal reasoning, is, for
the most part (UK/US style), reasoning
by analogy. For any given case, a lawyer looks to take
the facts,
the desired outcome and then seek to make an analogy with a
previously
adjudicated case so that if the analogy holds up, the
desired outcome is achieved by
virtue of the over-arching desire of
the legal ecosystem to maintain consistency with
previous decisions.
There is perhaps no other field where formulating the right question
is as important as it is in law.
Having constructed
an analogy, initial entry points into the corpus of law can be identified
and
from there, the citation network works it magic to route you through
the bottomless
seas of content, to the most relevant stuff. The term
"most relevant" here is oftentimes
signaled by the
presence of lots of in-bound citations. I.e. in caselaw, if your
analogy brings you to case X and case X has been cited by lots of
other cases with
the outcome you are looking to achieve, and if case
X is still good law (has not been
repealed), then case X is a good
one to cite in your legal argument.
If this leveraging
of the citation network link topology reminds you of Google's
original page rank algorithm then you are on the right track.
Lawyers, perhaps to the surprise of computer science and math folk,
have been leveraging the properties of scale free network graphs[2] for centuries[3].
I said "legal
argument" above and this is another critical point in
understanding
what law actually is and how it works...The corpus of
law is not a place you go
to find black and white answers to black
and white questions. Rather, it is a place
you go with an analogy
you have formed in order to find arguments for and against
your
desired outcome from that analogy. It is a form of rhetoric. A form
of debate. It
is not a form of formulaic application of crisp rules
that generate crisp answers.
In short. It is not
mathematics in the sense that many computer science folks might
initially assume when they hear of talk of "rules" and
"decisions" and so on.
However it arguably is mathematics
in some other ways. Leveraging the citation graph
is a very
mathematical thing. Weighing up the pros and cons of legal argument
strategies often exhibits properties familiar from optimization
problems and game theory.
It is in these
latter senses of "mathematical" that most of the recent
surge in interest in computational law have arisen. In particular,
machine learning and neural network-centric approaches to artificial
intelligence are re-igniting interest in computational law after an
overall disappointing outcome in the Eighties. Back then,
rule-centric approaches prevailed and although there have been some
noticeable successes in areas such as income
tax calculation,
rules-based approaches have largely run out of steam in my opinion.
The citation network
- and in particular - how the citation network changed over time, is,
in my opinion, the key to unlocking computational law. I do not think it
is stretching
things to state that the citation network is the
underlying DNA that holds the world
of law together. Rather that
seek to replace this DNA - in all its magnificent power and complexity -
with
nice tidy lego-bricks of conditional logic and data objects, we need
to embrace it.
Of course it has its flaws. Nothing is perfect. But
it is the way it is, for the most
part, for good reasons. We will
make progress in computational law faster if more computing
folk
understand the world of law for what it is - as opposed to what they
might initially think it is at a high level, or perhaps wish it to
be.
I hope this series
of blog posts has helped in some small way, to show what it really
is.
At least, from my perspective which of course, is just one
persons opinion. As we have
seen in this series of posts on law -
"opinion" is as good as it gets in law. Again, finally,
this not a bug. It is a feature...In my opinion:-)
3 comments:
I really enjoyed reading this series of blog posts, found it very informative. Highly appreciated.
Thanks. I have a lot more I would like to add related to Deep Learning, Smart Contracts, Blockchain and NLP but I will need to take a little while to better formulate my thoughts on these into a series of posts. The rough plan is to close the loop back to John Searle's Chinese Room Argument in a hopefully useful way.
I am very much looking forward to it, there are so many implicit references to machine learning (for my biased brain almost all), actually these posts can be used to explain how deep learning techniques are performing better than other shallow machine learning techniques. In deep leaning we let the algorithm to make its own representation of the data and then chain of opinions, which should be aligned to the end result which also may be an opinion (that final opinion, human can also make sense of it). Like a big virtual box or a big virtual box containing several other virtual boxes inside.
Though, I thought earlier it was a last blog post of this series. Good to hear that, that there are more.
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