Monthly Archives: September 2018

Legal IT

Machine Learning Applications for Lawyers

1 Comment

The first legal applications of Artificial Intelligence already appeared several decades ago, but they never really took off. That has changed over the last few years. A lot of the recent progress is thanks to advancements in Machine Learning (ML), Deep Learning (DL), and Legal Analytics (LA). As many lawyers are not familiar with these terms, we will first explain the concepts in this article. Then we will focus on some applications, and finish with some general considerations.

Let us start with the three terms Artificial Intelligence, Machine Learning and Deep Learning, and how they relate to each other. The first thing to know is that Artificial Intelligence is the broadest term. Machine Learning is a subset of Artificial Intelligence, and Deep Learning in turn is a subset of Machine Learning.

The Techopedia defines Artificial intelligence (AI) as “an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning, Problem solving.” Examples of legal AI applications that are not based on machine learning include, e.g., expert systems, decision tables, certain types of process automation (that focus on repetitive tasks), as well as simple legal chatbots that also focus on one or more specific tasks, etc.

Machine Learning (ML) is one branch of AI. It based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. It is a method of data analysis that automates analytical model building. To this end, it uses statistical techniques that give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) from the data, without being explicitly programmed.

In an article on TechRepublic, Hope Reese explains that Deep Learning (DL) “uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making. Deep learning can be expensive, and requires massive datasets to train itself on. That’s because there are a huge number of parameters that need to be understood by a learning algorithm, which can initially produce a lot of false-positives.”

The process of learning in both Machine Learning and Deep Learning can be supervised, semi-supervised or unsupervised.

When applied to legal data, Machine Learning is often referred to as Legal Analytics. It “is the application of data analysis methods and technologies within the field of law to improve efficiency, gain insight and realize greater value from available data.” (TechTarget)

Let us have a look at some of the applications of machine learning in the legal field. The applications that are available are not just for lawyers, but also, e.g., for courts and law enforcement.

In a previous article, we already mentioned Legal Research, eDiscovery and Triage Services. Legal databases are increasingly using AI to present you with the relevant laws, statutes, case law, etc. There are eDiscovery services for lawyers as well as for law enforcement that focus on finding relevant digital evidence. Both typically use triage services to rank the results in order of relevance.

Legal Analytics are also being used for due diligence (where the system creates and uses intelligent checklists),  and for document review, including contract review. In some cases, the system can even go a step further and assist with the writing of documents and contracts (Intelligent Document Assembly). Some more advanced examples of process automation, e.g. for divorce cases where the whole procedure is largely automated, also rely on ML algorithms.

One of the fields where legal analytics has been making headlines is predictive analysis: using statistical models, the system makes predictions. Predictive analysis is not just used by lawyers, but in the broader legal field: there also are for applications, e.g., for courts and for law enforcement. There are systems, e.g., for:

  • Crime prediction and prevention that predict future crime spots.
  • Pretrial Release and Parole, Crime Recidivism Prediction
  • Judicial analytics and litigation analytics predict the chances of success or what the anticipated outcome is in certain cases. These systems can e.g.  be as specific as to take previous rulings by the presiding judge into account.

ML is also successfully being used in crime detection. There are AI systems that monitor what cameras are registering, or that use a network of microphones to detect shots being fired. In the news recently was a story how facial recognition software was used to scan people attending a concert, which led to several arrests being made.

These are just some examples. An article that was recently published in Tech Emergence (“AI in Law and Legal Practice – A Comprehensive View of 35 Current Applications”) gives an overview of 35 applications.

So, a lot of progress has been made in recent years in the fields of legal analytics /  legal machine learning. Still, there are certain issues and limitations to take into account when it comes to the legal field. A first issue has to do with privacy and confidentiality. Law firms who want to use their client data may need consent by those clients, and will have to anonymize the data. They also have to remain GDPR compliant. A second issue has to do with bias: in a previous article we mentioned how these AI systems inherit our biases. A third issue has to do with transparency: most neural networks present a conclusion without explaining how it came to that conclusion. If used in criminal cases, this constitutes a violation of the rights of defence. In civil cases, too, judges have to explain their decisions, and merely referring to the decision an AI system made is not sufficient. Lastly, there also is a cognitive aspect to the work lawyers do, and at present the cognitive abilities of legal ML systems are (still) extremely limited. They do not, e.g., know how to appreciate or emulate common sense.

 

Sources:

 

Uncategorized

Online Reputation Management

Leave a comment

Do you, as a lawyer, pay much attention to your online reputation? You should. Because, in 2018, legal consumers are online consumers, as the following statistics clearly show:

  • 96% of people with a legal issue use the Internet first to find answers with regard to their problem.
  • 38% of people looking to hire a lawyer turn to the Internet first. (29% ask a friend or relative, 10% go directly to the local bar association, 4% rely on business directories like the Yellow Pages).
  • Once legal consumers have narrowed down their search to one or more potential lawyers, 74% of all legal consumers will visit that lawyer’s or law firms’ websites first, before taking action.
  • 74% of all legal consumers end up contacting a lawyer they found on the Internet, and of those 74%, 87% end up hiring that lawyer.
  • 70% of law firms have generated new cases through their website in the last year.

In these circumstances, Online Reputation Management (ORM) is more than highly recommended.

But how do you start managing your online reputation? After all, as the team of Blue Ocean points out: “Reputation, by its very definition is a nebulous, intangible and complex concept. Trust, along with an excellent reputation as a legal resource, cannot be directly measured like income and expenses.”

The Wikipedia describes Online Reputation Management as “the practice of attempting to shape public perception of a person or organization by influencing information about that entity, primarily online. (…) Specifically, reputation management involves the monitoring of the reputation of an individual or a brand on the internet, addressing content which is potentially damaging to it, and using customer feedback to try to solve problems before they damage the individual’s or brand’s reputation.”

In other words, ORM is about influencing how you are perceived on the Internet. You can affect this perception through multiple channels:

  • Your website often will be responsible for a potential client’s first impression of you.
  • Make sure to use testimonials.
  • You can publish a blog to help establish you as an authority in your field.
  • You can engage people via social media and discussion groups, by answering questions and offering free advice.
  • Online consumers typically also look for reviews on third party websites. It is recommended to respond to those reviews. (More on that below).
  • There are search results in search engines.
  • Not to be forgotten are your profiles in business directories.

Practically speaking, the first step is finding out what is being said about you and your firm. So you can start by doing an online search about your firm. Make sure, too, to find out what is being said on online review sites, as online consumers are eager to know what the experiences are of others who have used your services. You want to augment positive reviews, and to address negative reviews.

Addressing negative reviews can be tricky, especially since there are ethical considerations. You must make sure you never reveal any confidential information! As a rule, the best response to a negative review is to not respond with specific details, but to issue an apology instead, and to ask for personal feedback and to be contacted privately to address the matter.

In 2018, addressing fake news is also a concern. Make sure you do not give out false information about yourself (or your clients), and make sure to address any false information about you or your firm that might be available online.

Apart from addressing any factors that might damage your reputation, you can also more proactively start building a positive reputation through the channels mentioned above: your website, testimonials, blog articles, engagement with potential clients via social media and discussion groups, professional profiles in business directories, etc. Here, too, however, it is important to remain aware of ethical considerations, which may be specific to the bar association you belong to. Most bar associations do not allow lawyers to directly solicit clients. Some bar associations do not even allow lawyers to actively ask for reviews or testimonials.

 

Sources: