thoughtriver.comAI and a new way of looking at automated contract review

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The use of artificial intelligence software to identify possible items of concern in legal documents is well established. However, the real possibilities are only now being opened up with a new generation of systems.

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AI and a new way of looking at automated contract review

| thoughtriver.com |

intelligent contract pre-screening

thoughtriver.comAI and a new way of looking at automated contract review

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Breakthroughs in machine learning and natural language processing – ‘artificial intelligence’ – have attracted a great deal of interest and investment in the legal sector over the past few years (Goodman, 2016). Some commentators have hailed this technology as ushering in “the end of lawyers”, or at least greatly reducing the need for junior legal staff.

This may be premature. Even in routine legal work such as contract review, the ability of AI systems to scan rapidly through documents for points of interest does not remove the need for human professional expertise. In particular, what do we do with all the data the AI is throwing up? Meanwhile, the idea of exploiting the real power of AI – the knowledge gathered by such systems – is still in its infancy in law.

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Rank

32%

Adoption

Technology and communications

Industry

7 20%Consumer packaged goods

3 28%Financial services

9 17%Healthcare

5 22%Media and entertainment

11 16%Construction

2 29%Automotive and assembly

8 19%Retail

4 27%Energy and resources

10 17%Education

6 21%Transportation and logistics

12 13%Professional services

13 11%Travel and tourism

McKinsey AI report June 2017

Adoption rate of AI by industry

thoughtriver.comAI and a new way of looking at automated contract review

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Working life in corporate legal departments is dominated by pressure to review, negotiate, and then approve contracts, especially a constant influx of sales or distribution agreements. All too often, deals are agreed in outline by business teams and then passed to the legal department for a “quick once-over” of the contract before they are signed, and then the lawyers and therefore the deal can become bogged down with legalese. Inevitably, lawyers’ time is spent performing a first pass review of contracts that may not pose any great risk – but of course there is no way of knowing what level of expertise is required before the lawyer’s time is assigned to the job. This is because you simply do not know what is in a contract until you read it.

All this soaks up time: with most commercial agreements running to a minimum of 20 or 30 pages, a pre-screen and initial review takes between 1.5 to 3.5 hours.

This is very often a waste of highly-qualified professional abilities that could be more usefully deployed elsewhere. Lawyers are valuable assets and their time needs to be protected so they can turn their minds to supporting the business in a more strategic manner.

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thoughtriver.comAI and a new way of looking at automated contract review

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Hence the uncanny illusion that the software has the ability to extract meaning even from a precise set of words it has never encountered before. Whether this constitutes ‘understanding’ is a familiar problem in philosophy (Searle, 1980) but need not detain us here.

Where are we currently, and how does this AI technology work?

AI differs from conventional logic-based programs because the system’s programmers do not set out a series of algorithmic steps to achieve a prescribed end goal. Instead they ‘train’ the system how to get there, in a software model or a ‘neural network’. In legaltech, a common type of AI tool employs techniques of anomaly detection and pattern matching to find phrases that are topically similar to the training examples. Such pattern-matching can link a scanned image of a piece of legal text to an output meaning – “applicable jurisdiction”, for example – with an accuracy that increases with the number of training examples shown to the software.

As far as legal professionals are concerned, what matters in a technology solution is its ability to identify points of interest in a legal documentation in minutes, rather than days or weeks.

However, the ability to tag items of interest at lightning speed does not in itself improve productivity. Human expertise is still required to know what questions to ask – and what to do with the outputs. Without these skills, the AI is little more than a word-search, throwing up large numbers of matches which at best will need interpretation and at worst are irrelevant.

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On the surface, AI systems of the type we have seen deployed in law firms and in-house teams would be ideal for cutting this workload by fishing out the clauses that trigger certain alerts from mountains of documents. This ability enables a breakthrough in tasks such as due diligence – which is how the systems are marketed and why they are increasingly adopted by leading law firms. This type of automation is being used to extract information that is living within post-signature contracts. However, this does not address the requirement when reviewing pre-signature contracts, often on counterparty paper, coming into the business.

The legal department still requires expert human knowledge to make decisions about what to do with these new contracts that reflect new deals that the business is trying to close. Most of all, a human will be required to deliver the output required by the sales or procurement teams, which is invariably words to the effect of: “Are we good to go with this?” Rather than simply scanning the contract for data matches, what is needed to transform this process is a system that can review the contract and come up with a concise verdict analogous to a human lawyer’s conclusion – which will generally be along the lines of: “I’ve looked at this, and these five issues need careful consideration. Otherwise, we are OK.”

What we need to do is replicate the careful review performed by a well-trained lawyer. And this requires a different type of AI solution: automatic pre-screening.

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In summary so far:

Uses machine-learning to identify topic clusters and closely matching clauses across multiple documents Lack of consistency and precision when used to review individual contracts Useful for due diligence exercises to find issues across multiple documents

Fully deterministic because machine learning is not employed Cannot extrapolate itself to new situations/contract types Useful for automating contract workflows or triaging review assignments to multiple stakeholders

Uses machine-learning to match clauses to a pre-trained “position” Uses playbook rules to determine risk of said “position” and suggest mitigation measures Useful for speeding up first-pass review of pre-signature contracts, especially on counterparty paper

Rule-based Pattern-matching Pre-screening

Legaltech solutions employ some, or all of the above types of approaches in addressing the problems encountered by legal departments.

Now let us examine this third type of automation further.

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Contract pre-screening – a new category of legal AI

Two use-cases will be immediately obvious:

> In a merger or acquisition or major event such as a legislative change (the UK’s impending departure from the EU is a classic example). This is a typical due diligence scenario where quick answers are needed about thousands of contracts to identify where issues might lie.

> Where a first pass review is needed of a specific contract. This is where the ability to derive a top-level recommendation based on hundreds of questions (but not hundreds of answers) is most useful. This is known

as pre-screening and has a direct impact on a business’ deal velocity. New deals that are being negotiated by sales or procurement make it to the point of a draft contract, and this pre-screening software then replicates a lawyer’s review and reads, reviews, and assigns risks and a recommendation to the document.

Generating this kind of output (decisions) from an AI system requires a completely different approach to system architecture. Rather than asking a few questions of a document to get a large number of potential matches, the system needs to ask and answer many hundreds or even thousands of questions to find and then extract a few core issues.

The system developed by ThoughtRiver, for example, uses this approach to draw out issues that business managers are interested in, to create a holistic view. It is done by a combination of machine learning and traditional decision-tree.

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Obviously, this task could be carried out by human lawyers, but ThoughtRiver’s AI can ensure that it is done quickly, reliably, and consistently. And the results can be displayed on an on-screen dashboard alerting legal teams to upcoming issues on which professionals need to be deployed. This is a much more subtle decision than simply tagging every contract according to pre-set criteria such as valuation or renewal date. Many contracts can be allowed to lapse without the legal team needing to become involved – it all depends on context, which depends on developments across the organisation.

But automated contract pre-screening is not just about these dive-in, dive-out scenarios. It also enables enterprises to look at their contract inventories in quite a new way – and by so doing create a wholly new knowledge resource. In this approach, ThoughtRiver’s software builds up a picture of every contract throughout its life-cycle, from an initial assessment of the draft to the exposure it creates with new legislative or other external developments, to dealing with the implications of its expiry. It also records positions taken by the enterprise along the way, for example how often it may be accepting indemnities or unlimited liability on agreements.

Context is

everything

This applies particularly to value: an agreement with a negligible headline value may turn out to be a very high-risk matter if it has the effect of giving away key intellectual property.

A new digital asset is created

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Life-cycle contract screening can also provide the intelligence to spot business opportunities. Such opportunities are often time-sensitive, such as a rapidly approaching renewal date or an ability to increase prices.

In these instances being able to alert stakeholders to the opportunity for a potential sale or revenue increase is immediately of obvious benefit to the organisation, when the alternative is to let it go unnoticed as a result of a manual contract management process. General counsels and business executives can also use the information generated to make data-driven business decisions rather than rely on anecdotal ‘hunches’ on estimations of where their contractual obligations sit.

Monitoring this new data repository for commercial value.

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The real breakthrough enabled by ThoughtRiver’s AI-powered contract review, however, may be beyond the individual enterprise: the creation of a new body of knowledge on good practice. It starts with the playbooks maintained by individual enterprises. There are the accumulated volume of corporate rules stating essentially: “We can accept this, we can not accept that.”

The playbook provides legal teams with a matrix of behaviour, so long as they are remembered, observed, and – especially – known to be observed. Crucially, the knowledge inside it reflects what is really going on, not a set of aspirations.

Digital playbooks

Not only can we now identify at a glance any contract that is incompatible with the playbook, or becomes so due to changes outside the organisation, but we can also start changing behaviour within.

As a first step, we can now understand whether the playbook’s rules are themselves fit for purpose, by the very simple criterion of whether they are playing out in practice.

For example, if the knowledge base shows that a particular opening negotiating position mandated by the playbook for a particular type of contract is always knocked back by the other party, it may be advantageous to save time and

other resources by dropping that position from initial drafts.

This is not a trivial point: today it is often impossible for organisations of any size to be confident that everyone is applying the rules consistently.

thoughtriver.comAI and a new way of looking at automated contract review

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