Algorithm - explain thyself!

This is an interesting piece on the opacity of the algorithms that run legal research platforms.

http://www.lawpracticetipsblog.com/2017/08/algorithms-that-run-legal-research.html

Digital machinery - in general - is more opaque than analog machinery. In years gone by, analog equipment could be understood, debugged, tweaked by people not involved in its original construction: mechanics, plumbers, carpenters, musicians etc. As digital tech has advanced, eating into those analog domains,  we appear to loosing some control over the "how" of the things we are building...

The problem, quite ironically, also exists in the world of digital systems. These are regularly redone from scratch when the "how" of the systems is lost, typically when the minds involved in
its original construction - the holders of the "how" - cease to be involved in its maintenance.

With Deep Learning, the "how" gets more opaque still because the engineers creating these systems cannot explain the "how" of the decisions of the resultant system. If you take any particular decision made by such a system and look for a "how" it will be an essentially meaningless, extremely long mathematical equation multiplying and adding up lots of individually meaningless numbers.

In part 15 of the What is Law series I have posited that we will deal with the opacity of deep learning systems by inventing yet more digital systems - also with opaque "hows" - for the purposes of producing classic logic explanations for the operation of other systems:-)

I have also suggested in that piece that we cannot, hand on heart, know if our own brains are not doing the same thing. I.e. working backwards from a decision to a line of reasoning that "explains" the decision.

Yes, I do indeed find it an uncomfortable thought. If deductive logic is a sort of "story" we tell ourselves about our own decision making processes then a lot of wonderful things turn out to be standing on dubious foundations.

Would the real copy of the contract, please stand up?

Establishing authenticity of digital materials is a topic I have worked on for a long time now in the the context of electronic laws. The UELMA act[1],  the best records rule[2], federal rules of evidence[3], the OAIS model[4]  etc.

Nearly a decade ago now, I wrote an article for ITWorld called "Would the real, authentic copy of the document please stand up? [5]

I happened across it again today and re-reading it, I find it all still relevant, but Smart Contracts are bringing a new use case to the fore. The authenticity and tamper-evidence and judicial admissibility of digital laws is - I admit -  a very specialist area.

Contracts on the other hand....well that is a much much bigger area and one that a much larger group of people are interested in.

All the same digital authenticity challenges apply but over the next while I suspect I will be updating my own corpus of language to cater for the new Smart Contracts eco-system.

Old digital authenticity terms like content addressable stores, fixity, idempotent rendering, registrar etc. look like they will all have new lives under new names in the world of Smart Contracts.

Plus ça change...

I am happy to see it happening for a number of reasons but one of them is that the challenges of digital authenticity and preservation of legal materials can only benefit from an injection of fresh interest in the problem from the world of contracts.

[1] http://www.uniformlaws.org/Act.aspx?title=Electronic%20Legal%20Material%20Act
[2] https://en.wikipedia.org/wiki/Best_evidence_rule
[3] https://www.rulesofevidence.org/
[4] https://en.wikipedia.org/wiki/Open_Archival_Information_System
[5] http://www.itworld.com/article/2781645/business/would-the-real--authentic-copy-of-the-document-please-stand-up-.html

LWB 360

What is Law? - part 15

Previously: What is Law? - part 14.

In part one of this series, a conceptual model of legal reasoning was outlined based on a “black box” that can be asked legal type questions and give back legal type answers/opinions. I mentioned an analogy with the “Chinese Room” used in John Searle's famous Chinese Room thought experiment[1] related to Artificial Intelligence.

Simply put, Searle imagines a closed room into which symbols (Chinese language ideographs) written on cards, can be inserted via a slot. Similar symbols can also emerge from the room.

To a Chinese speaking person outside the room inserting cards and and receiving cards back, whatever is inside the room appears to understand Chinese. However, inside the box is simply a mechanism that matches input symbols to output symbols, with no actual understanding of Chinese at all.

Searle's argument is that such a room can manifest “intelligence” to a degree, but that it is not understanding what it is doing in the way a Chinese speaker would.

For our purposes here, we imagine the symbols entering/leaving the room as being legal questions. We can write a legal question on a card, submit it into the room and get an opinion back. At one end of the automation spectrum, the room could be the legal research department shared by partners in a law firm. Inside the room could be lots of librarians, lawyers, paralegals etc. taking cards, doing the research, and writing the answer/opinion cards to send back out. At the other end of the spectrum, the room could be a fully virtual room that partners interact with via web browsers or chat-bots or interactive voice assistants.

Regardless of where we are on that spectrum, the law firm partners will judge the quality of such a room by its outputs. If the results meet expectations, then isn't it a moot point whether or not the innards of the room in some sense “understand” the law?

Now let us imagine that we are seeing good results come from the room and we wish to probe a little to get to a level of comfort about the good results we are seeing. What would we do to get to a level of comfort? Well, most likely, we would ask the virtual box to explain its results. In other words, we would do exactly what we would do with any person in the same position. If the room can explain its reasoning to our satisfaction, all is good, right?

Now this is where things get interesting. Imagine that each legal question submitted to the room generates two outputs rather than one. The first being the answer/opinion in a nutshell (“the parking fine is invalid : 90% confident.”). The second being the explanation “The reasoning as to why the parking fine is invalid is as follows....”). If the explanation we get is logical i.e. it proceeds from facts through inferences to conclusions, weighing up the pros and cons of each possible line of reasoning....we feel good about the answer/opinion.

But how can we know that the explanation given is actually the reasoning that was used in arriving at the answer/opinion? Maybe the innards of the room just picked a conclusion based on its own biases/preferences and then proceeded to back-fill a plausible line of reasoning to defend the answer/opinion it had already arrive at?

Now this is where things may get a little uncomfortable. How can we know for sure that a human presenting us with a legal opinion and an explanation to back it up, is not doing exactly the same thing?

This is an old old nugget in jurisprudence, re-cast into today's world of legal tech and Artificial Intelligence. Legal scholars refer to it as the conflict between so-called rationalist and realist models of legal reasoning. It is a very tricky problem because recent advances in cognitive science have shone a somewhat uncomfortable light on what actually goes on in our mental decision making processes.

Very briefly, we are not necessarily the bastions of cold hard logic that we might think we are. This is not just true in the world of legal reasoning, by the way. The same is true for all forms of reasoning including – shock! - mathematicians.

Recent research[2][3] suggests that human legal reasoning is best viewed as a bi-directional process that oscillates between working forward from premises/facts and working backwards from conclusions to supporting premises/facts.

Mention was previously made of the feature of law whereby different legal minds can look at the same corpus and come up with different conclusions. In this respect, our virtual legal reasoning room is just another source of a legal opinion. Another legal “mind” if you will. The quality of the opinions produced are judged on their merits – the explanations - not on its actual means of production of answers/opinions.

To this way of thinking, lawyers should enthusiastically embrace these new virtual research assistants that are emerging. Who wouldn't see benefit from being able to get other legal “minds” to look at a legal question and offer opinions. Who wouldn't see benefit from being able to ask such a virtual research assistant to argue for and against a given assertion to help sharpen a line of reasoning for use in a legal opinion or in a court room?

Some see problems with the modern machine learning approach to legal AI because of the inability of these systems to explain their conclusions in the form of classic forward-chaining logic. I do not see this being a problem in practice because these systems will develop ways to explain their opinions. They will most likely do it as a completely separate activity. We may know for a fact that they  are reasoning "backwards" but we can never know if the same isn't true for the opinions given by our fellow humans – including the opinions we provide to ourselves!

We have a tendency to get caught up in the notion of intelligent machines replacing humans. We look at the incredible progress machines have made in playing Chess of Go, identifying faces in photographs etc. and some wonder how long it will be before the machines replace the lawyers. I believe there is a qualitative difference between practicing law and, say, playing chess that gets glossed over in the excitement about AI in law.

In chess, there is a small number of variables and a huge, huge set of permutations/combinations of possible moves. Moreover, the key variables can all be encoded for the machine to work with. This makes this sort of game-playing a great candidate for complete mechanisation. i.e. getting to the point where the machine can play the game unaided.

Not so with law. A lawyer's reasoning processes invariable are a lot more expansive covering variables such as the overall goals of the client, trade offs between time and opportunity cost, reputational risk factors, budget constraints, team dynamics etc. etc. On top of these, I have argued in previous posts that the entire legal system is not and cannot be, reduced to a set of rules – no matter how large the set of rules might be envisaged to be.

Rather than think of machines are replacements for lawyers, better to think of machines as augmenting lawyers in my opinion. Machines are no longer confined to document management and mechanical search&retrieval. Machines are increasingly offering opinions as to what is relevant. They have been doing that for quite some time - from the dawn of search result ranking - but in recent years their role as sources of opinion has grown significantly. This trend will continue apace in my opinion. I think we will soon see the day when every lawyer in private practice has access to legal virtual assistants that can provide answers/opinions to supplement the lawyers own research/experience and that of their colleagues.

If I were a professional chess player, I would be a lot more worried about career viability in the age of intelligent machines than I would be as an lawyer, or an accountant or a medical doctor. Yes, intelligent machines will impact these professions as more and more of the mechanizable tasks become mechanized. But the machines can only compute with what they have visibility of and it is in all the stuff that the machines cannot have visibility of that the 21^st Century professionals will live.

A good example of this can be found in the world of contracts and in particular, the emerging world of “smart contracts” which is where we will turn to next.

[1] http://www.iep.utm.edu/chineser/

[2] http://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=5261&context=uclrev

[3] http://home.sandiego.edu/~baber/logic/ethics.html

The Rise of the Digitally Augmented Lawyer

The Rise of the Digitally Augmented Lawyer

Blockchain and Byzantium

Establishing authenticity - "single sources of truth" is a really important concept in the real world and in the world of computing.  From title deeds, to contracts, to laws and currencies, we have evolved ways of establishing single sources of truth over many centuries of trial and error.

Knowingly or not, many of the ways of solving the problem rely on the properties of physical objects: clay tablets (Code of Hammurabi), Bronze Plates (The Twelve Tables of Rome), Goat Skin (Celtic Brehon Laws). Typically, this physicality is mixed in with a bit of trust. Trust in institutions. Trust in tamper evidence. Trust in probabilities.

Taken together: the physical scheme aspect, plus the trust aspect, allows the establishment of consensus. It is consensus, at the end of the day, that makes all this stuff work in the world of human affairs. Simply put, if enough of us behave as though X is the authentic deed/deposition/derogation/dollar then X is, ipso facto, for all practical purposes, the real deal.

In the world of digital data, consensus is really tricky because trust becomes really tricky. Take away the physicality of objects and establishing trust in the truth/authenticity of digital objects is hard.

Some folk say that blockchain is slow and inefficient and they are right - if you are comparing it to today's consensus as to what a "database" is.

Blockchain is the way it is because it is trying to solve the trust problem. A big part of that is what is called Byzantine Consensus. Basically how to establish consensus when all sorts of things can go wrong, ranging from honest errors to sabotage attempts.

The problem is hard and also very interesting and important in my opinion. Unfortunately today, many folks see the word "database" associated with blockchain and all they see is the incredible inefficiency and cost per "transaction" compared to, say, a relational database with ACID properties.

Yes, blockchain is a truly dreadful "database" - if your metric for evaluation is the same as the use cases for relational databases.

Blockchain is not designed to be one of those. Blockchain is the way it is because byzantine consensus is hard. Is it perfect? Of course not but a proper evaluation of it requires looking at the problems it is trying to solve. Doing so, requires getting past common associations most people carry around in their heads about what a "database" is and how it should behave/perform.

Given the unfortunate fact that the word "database" has become somewhat synonymous with the term "relational database", I find it amusing that Blockchain has itself become a byzantine consensus problem. Namely, establishing consensus about what words like  "database" and "transaction" and "trust" really mean.

What is Law? - part 14

Previously: What is Law? - part 12a

Mention has been made earlier in this series to the presence of ambiguity in the corpus of law and the profound implications that the presence of ambiguity has on how we need to conceptualize computational law, in my opinion.

In this post, I would like to expand a little on the sources of ambiguity in law. Starting with the linguistic aspects but then moving into law as a process and an activity that plays out over time, as opposed to being a static knowledge object.

In my opinion, ambiguity is intrinsic in any linguistic formalism that is expressive enough to model the complexity of the real world. Since law is attempting to model the complexity of the real world, the ambiguity present in the model is necessary and intrinsic in my opinion. The linguistic nature of law is not something that can be pre-processed away with NLP tools, to yield a mathematically-based corpus of facts and associated inference rules.

An illustrative example of this can be found in the simple sounding concept of legal definitions. In language, definitions are often hermeneutic circles[1] which are formed whenever we define a word/phrase in terms of other words/phrases. These are themselves defined in terms of yet more words/phrases, in a way that creates definitional loops.

For example, imagine a word A that is defined in terms of words B, and C. We then proceed to define both B and C to try to bottom out the definition of A. However, umpteen levels of further definition later, we create a definition which itself depends on A – the very thing we are trying to define - thus creating a definitional loop. These definitional loops are known as hermeneutic circles[1].

Traditional computer science computational methods hate hermeneutic circles. A large part of computing consists of creating a model of data that "bottoms out" to simple data types. I.e. we take the concept of customer and boil it down into a set of strings, dates and numbers. We do not define a customer in terms of some other high level concept such as Person which might, in turn, be defined as a type of customer. To make a model that classical computer science can work on, we need a model that "bottoms out" and is not self-referential in the way hermeneutic circles are.

Another way to think about the definition problem is in term of Saussure's linguistics[2] in which language (or more generically "signs") get their meaning because of how they differ from other signs - not because they "bottom out" into simpler concepts.

Yet another way to think about the definition problem is in terms of what is known as the descriptivist theory of names[3] in which nouns can be though of as just arbitrary short codes for potentially open-ended sets of things which are defined by their descriptions. I.e. a "customer" could be defined as the set of all objects that (a) buy products from us, (b) have addresses we can send invoices to, (c) have given us their VAT number.

The same hermeneutic circle/Sauserrian issue arises here however as we try to take the elements of this description and bottom out the nouns they depend on (e.g., in the above example, "products", "addresses", "invoices" etc.).

For extra fun, we can construct a definition that is inherently paradoxical and sit back as our brains melt out of our ears trying to complete a workable definition. Here is a famous example:

The 'barber' in town X is defined as the person in town X who cuts the hair of anyone in town who do not choose to cut their own hair.

This sounds like a reasonable starting point for a definition of a 'barber', right? Everything is fine until we think about who cuts the barber's hair[4].

The hard facts of the matter are that the real world is full of things we want to make legal statements about but that we cannot formally define, even though we have strong intuitions about what they are. What is a "barber"? What is the color "red"? Is tomato ketchup a vegetable[5]? What is "duty"? What is "ownership"? etc. etc. We all carry around intuitions about these things in our heads, yet we struggle mightily to define them. Even when we can find a route to "bottom out" a definition, the results often seem contrived and inflexible. For example we could define "red" as 620–750 nm on the visible spectrum but are we really ok with 619nm or 751nm being "not red"?

Many examples of computing blips and snafus in the real world can be traced to the tendency of classical computing to put inflexible boundaries around things in order to model them. What does it mean for a fly-by-wire aircraft to be "at low altitude"? What does it mean for an asset to be trading at "fair market value"? The more we attempt to bottom these concepts out into hard numeric ranges - things classical computing can easily work with - the more we risk breaking our own intuitions with the real world versions of these concepts.

If this is all suggesting to you that computational law sounds more like a problem that requires real numbers (continuous variables) and statistical calculations as opposed to natural numbers and linear algebraic calculations, I think that is spot on.

I particularly like the concept of law as a continuous, analog process as it allows a key concept in law to be modeled more readily - namely the impact of the passage of time.

We have touched on the temporal aspects already but here I would like to talk a little about how the temporal aspects impact the ambiguity in the corpus.

As time passes, the process of law will itself change the law. One of the common types of change is a gradual reduction in levels of ambiguity in the corpus. Consider a new law which needs to define a concept. Here is how the process plays out, in summary form.

• A definition is created in natural language. Everybody involves in the drafting knows full well that definitions cannot be fully self-contained and that ambiguity is inevitable. In the interests of being able to actually pass a law before the heat death of the universe, a starter definition is adopted in the law.
• As the new law finds its way into effect, regulations, professional guidance notes etc. are created that refine the definition.
• As the new law/regulations/professional guidance impacts the real world, litigation events may happen which result in the definition being scrutinized. From this scrutiny, new caselaw is produced which further refines the definition, reducing but never completely removing, the amount of ambiguity associated with the defintion.

A closely related process - and a major source of pragmatic, pre-meditated ambiguity in the process of law - is contracts. While drafting a contract, the teams of lawyers on both sides of the contract know that ambiguity is inevitable. It is simply not possible, for all the reasons mentioned above, to bottom out all the ambiguities.

The ambiguity that necessarily will remain in the signed contract is therefore used as a negotiating/bargaining item as the contract is being worked. Sometimes, ambiguity present in a draft contract gives you a contractual advantage so you seek to keep it. Other times, it creates a disadvantage so you seek to have it removed during contract negotiations. Yet other times, the competing teams of lawyers working on a contract with an ambiguity might know full well that it might cause difficulties down the road for both sides. However it might cost so much time and money to reduce the ambiguity now that both sides let it slide and hope it never becomes contentious post contract.

So to summarize, ambiguity in law is present for two main reasons. Firstly there is ambiguity present that is inevitable because of what law is trying to model - i.e. the real world. Secondly, there is ambiguity present that is tactical as lawyers seek to manipulate ambiguities so as to favor their clients.

Next up: Part 15

[1] https://en.wikipedia.org/wiki/Hermeneutic_circle

[2] https://en.wikipedia.org/wiki/Ferdinand_de_Saussure

[3] https://en.wikipedia.org/wiki/Descriptivist_theory_of_names

[4] https://en.wikipedia.org/wiki/Barber_paradox

[5] https://en.wikipedia.org/wiki/Ketchup_as_a_vegetable