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Machine learning: A sub-category of AI
Before we dive into the topic of machine learning, we must first clarify a few terms. Starting with artificial intelligence (AI) or artificial intelligence (AI). In the simplest case, AI is the use of machines to solve complex problems. Many processes can be carried out in the area of contract management Optimize and drastically improve using AI.
Machine learning is an area of AI that deals with developing systems that can “learn” patterns from data and then use those patterns to make predictions when presented with new data that they haven't seen before. Machine learning is usually a two-stage process.
The amount of data is decisive
It is known that machine learning usually requires a large data set. In mathematics, this is a well-known phenomenon. If you want to ascribe a high probability with statements, then a large data set is required. Since machine learning accesses statistics, this rule also applies here without restriction. This means that analyses that are highly likely to provide meaningful evaluations of your contract process require large data sets.
Some of these data sets must first be used to develop the model. Unlike conventional algorithms, which are written directly by humans for a known pattern, a machine learning algorithm is given the task of identifying a pattern from the data that leads to a known result.
Conclusion or prediction
The finished model can now be fed with new data unknown to the model. The machine learning model then makes predictions for the results of the new data series based on the known training data.
What significance does machine learning have for the legal sector?
There are two major areas of machine learning that are also of great interest to the legal sector:
Supervised Learning
Supervised learning is one of the easier tasks for machine learning to extract and analyze contract data. As part of supervised learning, data points are provided with so-called labels. Data points can be entire contracts, paragraphs, or even just individual words. Enriching the data with labels makes it easier for machine learning algorithms to recognize patterns in the data. The patterns learned, such as the recognition of paragraphs in contracts, can then be carried out independently by the machine for new data sets.
The enrichment of data with labels in supervised learning makes it easier for machine learning algorithms to recognize patterns in the data
However, a clear disadvantage of supervised learning compared to other methods is the fact that human input is required to recognize patterns within data. Especially when it comes to evaluating thousands of contracts, the additional effort is substantial.
Unsupervised Learning
In the case of unsupervised learning, there is no need to categorize the data by humans. This enables an automated extraction of contract data, which means that the machine also tries to identify similarities in the data in this case. However, the additional labeling information is missing for training machine learning algorithms. Identifying patterns within disordered data sets is therefore usually more difficult. As in the first case, it is once again up to humans to interpret the connections that may have been discovered.
Human control is particularly necessary in unsupervised learning, as the principle of sham correlation known in statistics, which poses the question of causality, can only be ruled out by humans.
Unsupervised learning is often used to detect anomalies in contracts that cannot be identified with simple labels. This is valuable information, particularly in the context of due diligence analyses.
Unsupervised learning is often used to detect anomalies in contracts that cannot be identified with simple labels.
The problems of machine learning for text analysis
The difficulty that machine learning algorithms have with text analysis is that it is often much more difficult to convert text passages into a numeric representation that is able to capture all the information that is available to a normal person when they read the text. We can provide a machine with words and syntax that can be expressed numerically, but it is much more difficult to express the semantics, meaning, and context behind a particular document.
Unlike when analyzing images, where a large number of pixels can be changed without affecting the image's perception, the meaning of a section of text can change significantly if you change small details in the text; even tiny details such as a comma can completely change the meaning of a sentence.
Which contract data can be extracted?
metadata
This data is already available in numerical form and can be recorded and processed very easily during analysis. Data in this category includes duration of processing, audit loops, number of processing and participating persons, and the quality of committed lawyers. All of this helps contract processes become smarter and more efficient. The metadata is the layer above the actual contract.
Data in the contracts themselves
The data in the actual contracts themselves is much more difficult to process and evaluate, as semantics often cannot be recorded in numerical structures that are necessary for machine learning and small details are decisive. For our models, we look at text analysis on 3 levels:
- Word level: At this level, valuable information can be extracted from individual words or groups of words. This could be the start or end date of a contract, the identification of the parties to the contract, or the established place of jurisdiction.
- Paragraph level: The analysis of individual paragraphs is usually used to determine whether a contract contains a specific type of clause (such as a confidentiality clause or a liability clause), or it can be determined how similar the clauses in two contracts are.
- Contract level: At contract level, the type of contract and the industry for which the contract was written can be classified.
Regardless of how and where data is collected and processed, the important point of machine learning is to always be aware of why we model contract data in the first place: to solve problems for customers.
Machine learning can be a great advantage in a company where several lawyers usually invest a great deal of time and effort in the manual evaluation and analysis of contract clauses. Since artificial intelligence significantly accelerates this process, it not only saves time, effort and resources, but also ultimately enables more contract negotiations to be completed in a shorter period of time.
Is machine learning the ultimate solution?
Even though many market participants portray artificial intelligence as the holy grail for all problems, it is currently just a tool in the kit of the inclined software engineer.
Machine learning should therefore never be used for its own sake, for example to put a missing marketing message on a website or to convince investors of technical expertise. Even if artificial intelligence is used, the end customer is simply interested in solving the problem. And that should be at the forefront of every reputable company. Good machine learning algorithms are therefore always embedded and are an integral part of the existing software design for solving a specific problem. If the design works, users shouldn't even notice whether machine learning is involved.
