Tag Archives: artificial intelligence

Legal AI and Bias

Justice is blind, but legal AI may be biased.

Like many advanced technologies, artificial intelligence (AI) comes with its advantages and disadvantages. Some of the potentially negative aspects of AI regularly make headlines. There is a fear that humans could be replaced by AI, and that AI might take our jobs. (As pointed out in a previous article, lawyers are less at risk of such a scenario: AI would perform certain tasks, but not take jobs, as only 23% of the work lawyers do can be automated at present). Others, like Elon Musk, predict doomsday scenarios if we start using AI in weapons or warfare. And there could indeed be a problem there: what if armed robotic soldiers are hacked, or have bad code and go rogue? Some predict that superintelligence (where AI systems become vastly more intelligent than human beings) and the singularity (i.e. the moment when AI systems become self-aware) are inevitable. The combination of both would lead to humans being the inferior species, and possibly being wiped out.

John Giannandrea, who leads AI at Google, does not believe these are the real problems with AI. He sees another problem, and it happens to be one that is very relevant to lawyers. He is worried about intelligent systems learning human prejudices. “The real safety question, if you want to call it that, is that if we give these systems biased data, they will be biased,” Giannandrea said.

The case that comes to mind is COMPAS, which is risk assessment software that is used to predict the likelihood of somebody being repeat offender. It is often used in criminal cases in the US by judges and parole boards. ProPublica is a Pulitzer Prize winning non-profit news organization. It decided to analyse how correct COMPAS was in its predictions. They discovered that COMPAS’ algorithms correctly predicted recidivism for black and white defendants at roughly the same rate. But when the algorithms were wrong, they were wrong in different ways for each race. African American defendants were almost twice as likely to be labelled a higher risk where they did not actually re-offend. And for Caucasian defendants the opposite mistake was made: they were more likely to be labelled lower risk by the software, while in reality they did re-offend. In other words, ProPublica discovered a double bias in COMPAS, one in favour of white defendants, and one against black defendants. (Note that COMPAS disputes those findings and argues the data were misinterpreted).

The problem of bias in AI is real. AI is being used in more and more industries, like housing, education, employment, medicine and law. Some experts are warning that algorithmic bias is already pervasive in many industries, and that almost no one is making an effort to identify or correct it. “It’s important that we be transparent about the training data that we are using, and are looking for hidden biases in it, otherwise we are building biased systems,” Giannandrea added.

Giannandrea correctly points out that the underlying problem is a problem of lack of transparency in the algorithms that are being used. “Many of the most powerful emerging machine-learning techniques are so complex and opaque in their workings that they defy careful examination.”

Apart of all the ethical implications, the fact that it is unclear how the algorithms come to a specific conclusion could have legal implications. The U.S. Supreme Court might soon take up the case of a Wisconsin convict who claims his right to due process was violated when the judge who sentenced him consulted COMPAS. The argument used by the defence is that the workings of the system were opaque to the defendant, making it impossible to know for what arguments a defence had to be built.

To address these problems, a new institute, the AI Now Institute (ainowinstitute.org) was founded. It produces interdisciplinary research on the social implications of artificial intelligence and acts as a hub for the emerging field focused on these issues. Their main mission consists of “Researching the social implications of artificial intelligence now to ensure a more equitable future.” They want to make sure that AI systems are sensitive and responsive to the complex social domains in which they are applied. To that end, we will need to develop new ways to measure, audit, analyse, and improve them.

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Legal Chatbots

One year ago, we wrote about the world’s first robot lawyer. Donotpay.co.uk was created by Joshua Browder. It is a website with a chatbot that started off with a single and free legal service: helping to appeal unfair parking tickets. When the article was published, the services was available in the UK, and in New York and Seattle. At the time, it had helped overturn traffic tickets to the value of 4 million dollars. Apart from appealing parking tickets, the website could already assist you, too, in claiming compensation if your flight was delayed. Since then, a lot has happened. By now, DoNotPay has successfully appealed traffic tickets to the amount of 10 million dollars. But, more importantly, its activities have expanded considerably. And in the last year, several other legal chatbots have seen the light of day, as well.

Let us start with DoNotPay. A first important expansion came in March 2017, when it started helping refugees claim asylum. Using its chatbot interface, DoNotPay can offer free legal aid to refugees seeking asylum in the US and Canada, and assists with asylum support in the UK.

A second, and far more massive expansion followed only days ago, on 12 July 2017, when DoNotPay started covering a much broader range of legal issues. Its new version can offer free assistance in 1,000 legal areas, and does so across all 50 US states, as well as in the UK. It can now, e.g., assist you in reporting harassment in the workplace, or to make a complaint about a landlord; or it can help you ask for more parental leave, dispute nuisance calls, fight a fraudulent purchase on your credit card… The new DoNotPay covers consumer and workplace rights, and a host of other issues.

Browder didn’t stop there. Because he wants to address the issues of ‘information asymmetry’ and ‘inequality of arms’, as of 14 July 2017, DoNotPay is opening up so that anyone can create legal bots for free, with no technical knowledge. If you want to create your own free legal chatbot, all you have to do is fill in this downloadable form, and send it to automation@donotpay.co.uk.

Another interesting legal chatbot, is Law Bot, which was created by a team of Cambridge University law students, consisting of Ludwig Bull, Rebecca Agliolo, Nadia Abdul and Jozef Maruscak. When Law bot was launched, it only dealt with aspects of criminal law in the UK. More specifically, the bot wanted to inform people who had been the victim of a crime about their legal rights. What had motivated the creators, was the observation that most advice from lawyers on legal rights of the victims of a crime felt like it was written mainly for the use of other lawyers, rather than to help inform the general public, who were in fact the people most in need of the information. The first version of Lawbot guided its users through a series of questions and answers that helped them to assess what, from a legal perspective, may have happened to them and what they should do next, such as to formally report a crime to the police.

A second Law Bot initiative was Divorce Bot. It asks its users questions via an internet-based interface to guide them through the early days of a divorce. The chatbot explores different scenarios with them, and helps clarify their exact legal position. It also explains legal terms that are commonly used in divorce, such as ‘irretrievable breakdown‘ and ‘decree nisi‘, and provides a comprehensive breakdown of the divorce process. It gives a breakdown of the costs and forms needed, too. This way, people (in the UK) know exactly what to expect, even before they talk to a lawyer.

One of Law Bot’s co-founders also launched an AI-driven case law search engine, called DenninX. The free application’s aim is to help lawyers and law students conduct legal research on English case law by making use of AI technology, such as natural language pre-processing and machine learning.

24 July 2017 is the launch date of a new and more expanded version of Law Bot, called Lawbot-X.  Lawbot-X will now cover seven countries: Great Britain, the US, Canada, Hong Kong, Singapore, Australia and New Zealand. It will also be available in Chinese, for markets such as Hong Kong. The new bot further adds a case outcome prediction capability to assess the chance of winning a legal claim that the bot has analysed. The free legal bot will also operate from a new platform and will be hosted on Facebook Messenger.

[Update 25 November 2017: in October 2017, Lawbot changed its name to Casecrunch].

Another useful chatbot for legal consumers is Billy Bot. Unlike the DoNotPay and Law bot chatbots, Billy Bot does not offer legal assistance, but helps you find a lawyer, barrister or solicitor, in the UK. Billy Bot was created by Stephen Ward, a career barristers’ clerk, and founder of clerk-oriented technology company Clerksroom. Billy Bot can interface with members of the public about some of the same preliminary legal questions that barristers’ clerks often handle. It can currently refer users to appropriate legal resources and pull information from the 350 barristers’ offices. Ward intends to give it access to other systems, including scheduling and case management capabilities. It currently answers questions on LinkedIn.

Next, we have Lawdroid, which was created by Tom Martin. Lawdroid is an intelligent legal chatbot that can help entrepreneurs in the US get started by incorporating their business on a smartphone for free. No lawyer is required. Lawdroid is available on Facebook Messenger. Lawdroid, too, has expanded its services, and the company that created the bot, now also makes legal chatbots for lawyers. Referring to the important rise of chatbots, they point out that there are over 100.000 of them already on Facebook.

[Update 25 November 2017: corrected an item with regard to Lawdroid].

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AI and Contracts

Artificial Intelligence (AI) is changing the way law is being practiced. One of the areas where AI, and more specifically Machine Learning (ML) has been making great strides recently is contract review. The progress is not even limited to reviewing contracts: automated contract generation, negotiation, e-signing and management are fast becoming a reality.

Using AI for contracts is the result of an ongoing evolution. Ever since lawyers started using word processors, they have tried to automate the process of creating contracts. Using advanced macros allowed them to turn word processors into act generators that used smart checklists to fill out templates and add or remove certain clauses. But now the available technology is sufficiently advanced to take it all a few steps further.

Some years ago, commercial lawyer Noory Bechor came to the realization that 80 percent of his work was spent reviewing contracts. As a lot of the work involved in reviewing contracts is fairly repetitive in nature, he figured the service could be done much cheaper, faster, and more accurately if it was done by a computer. So, in 2014, he started LawGeex, which probably was the first platform for automatized contract review. Users can upload a contract to LawGeex.com, and, within a reasonably short period of time (an hour on average), they receive a report that states which clauses do not meet common legal standards. The report also warns if any vital clauses could be missing, and where existing clauses might require further attention. All of this is done automatically, by algorithms.

By now, there are other players on the contract review market as well, and the technology is evolving further. At present, AI technology is able to scan contracts and decipher meaning behind the text, as well as identify problem areas that might require human intervention. This technology can scan millions of documents in a fraction of the time it would take humans (think ‘hours’ as opposed to ‘days’ or ‘weeks’). As a result, AI contract review has reached a point where it can already do 80% of the work a lawyer used to do. For the remaining 20%, it can, at present, not reach the level of skill and comprehension of a human attorney. AI contract review, therefore, focuses attorneys’ efforts on higher-level, nonstandard clauses and concerns, and away from more manual contract review obligations.

The progress made in Machine Learning algorithms means the usage of AI is not limited to contract review. Juro is a company that tries to automate the whole contracts workflow. It has developed an integrated workflow system that allows companies to save time on contracts through automated contract generation, negotiation, e-signing and management of contracts. For this, it relies on machine learning algorithms that try to understand the data within contracts and learn from it. This can be done, e.g., by analyzing all the contracts in a company’s ‘vault’ of historical contracts. Based on these contract analytics, Juro can also provide so-called ‘negotiation heatmaps,’ where customers can see at a glance which of their contract terms are being most hotly negotiated. Knowing what other customers have negotiated can help you (based on data) decide what the contract terms should be and what you should agree to in negotiations.

Another interesting evolution is the idea of ‘smart contracts’. Stephen Wolfram, the founder of Wolfram Alpha, believes contracts should be computable, and that a hybrid code/legalese language should be developed. One of the main advantages of such language would be that it would leave less room for ambiguity, especially when it comes to the implications of certain clauses. Computable contract language becomes more valuable to the legal sector, once we start using ‘smart contracts’ that are self-executing. There is also already some interesting work in this area, namely by Legalese.com based in Singapore. If law is going to be made computable then the world needs two things: lawyers who can code and a legal computer language that is an improvement on today’s legalese.

The next step would then be to move from ‘smart’ contracts to ‘intelligent’ contracts. Smart Contracts resemble computer code more than typical legal documents, relying on programing to create, facilitate, or execute contracts, with the contracts and conditions stored on a blockchain, or a distributed, relatively unhackable ledger. Intelligent contracts would not just be smart, but also rely on artificial intelligence (hence ‘intelligent’ contracts). In the words of Kevin Gidney, intelligent contracts would use an AI system that “is taught to continually and consistently recognize and extract key information from contracts, with active learning based on users’ responses, both positive and negative, to the extractions and predictions made”.

 

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Artificial Intelligence and Law

Legal Artificial Intelligence has made the news’ headlines often, recently. There are chat bots, e.g., that help you appeal against a parking ticket (www.donotpay.co.uk), or give you a first advice if you are considering a divorce (divorce bot). There is software that predicts the likely outcome of court cases. IBM offers legal AI services for eDiscovery and legal research with its Watson supercomputer: Ross, as the service is called, uses Natural Language Processing, and can also be used for cognitive computing, e.g., to review contracts (www.rossintelligence.com). Then there is RPA (Robotics Process Automation) who are creating software robots for law firms. The idea is that any repetitive task that lawyers and paralegals do at a computer can be performed by a software robot. These are two examples of AI doing the work that is often done by paralegals and lawyers.

So, what is the current state of affairs? What is being used and developed within law firms, and what do service providers have to offer when it comes to artificial intelligence for lawyers? Basically, there are three main pillars, where AI is currently being used:

  • Research and Data Analysis: Legal Research, eDiscovery, triage services for those two, predictive systems, other analytics (statistics, correlations, etc.),
  • Cognitive systems: expert systems, contract review
  • Task automation: bots (advice, automate repetitive tasks)

Let us explore these a bit further.

Legal Research: Looking for legal information that is relevant to a case you’re handling? Legal databases are increasingly using AI to present you with the relevant laws, statutes, case law, etc.

eDiscovery: While legal research deals with legal information, eDiscovery focuses on finding evidence that is stored in a digital format. More and more evidence is stored electronically, on computers, smart phones, but also in the cloud (think, e.g., of social media). The task of finding evidence that is pertinent to a case more and more becomes like looking for a needle in a haystack. No wonder that AI is increasingly being used to assist in eDiscovery.

Triage services: So, you have used programs for legal research and eDiscovery. Often that is just the first round, i.e. finding relevant information. If you’re confronted with thousands of results, you need a second round, which is the triage: determining what is most relevant and sorting the results accordingly. Triage services are often built into the software you are using, but are also being offered by independent third party service providers.

Predictive systems: A previous article was dedicated to a software system that could accurately predict the outcome of court cases dealing with Human Rights. It was right in 79% of the cases. Several service providers have announced similar products for other areas of law. But predictive systems are not limited to case outcome predictions. US data scientists have, e.g., developed a new algorithmic analysis they believe could help judges reduce crime by up to 25% in certain situations. The software performs a risk assessment and advises a judge whether a defendant awaiting their court date for an alleged crime should or should not be released to go home before the trial starts.

Other analytics: Machine learning and data mining is also used to provide us with (other) statistics, relevant correlations, etc.

Expert systems: The first cognitive systems already were developed in the 90s. Expert systems are intelligent checklists that have the built-in ability to reason, i.e., perform logical operations and functions.

Contract review: One area where cognitive systems are shining at present is contract review. Important, e.g., is the ability to point out clauses that are lacking in a contract.

Task automation: Bots are intelligent software robots that are created to automate specific tasks. In the introduction of this article, we gave the examples of chat bots that can give advice, and other bots that are being designed to perform any task a lawyer or paralegal does repetitively on a computer.

What does this all mean for lawyers? Richard Tromans, at www.artificiallawyer.com, sums it up perfectly:  ” … the arrival of AI marks a Renaissance for the legal industry because it permits lawyers to be real lawyers again and not tired process units counting down the hours of their day. After all, isn’t the definition of a lawyer a person who is doing something special in society, i.e. taking on a client’s problems and making it their duty to help them? Isn’t that why membership of the profession is so jealously guarded and so heavily regulated? If this is just any other office job, then why all the fuss to become a lawyer? But of course, it’s not just any other office job. In which case, maybe AI is the best thing that has happened to lawyers in many decades.”

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Legal Technology Trends for 2017

It is common, at the beginning of the year, to ponder upon what the year ahead will bring. Several experts have published their predictions for trends we can expect in legal technology, in 2017. So, what are they saying? Generally speaking, they expect lawyers to become more mobile, more collaborative (using the cloud do to do), and more responsive (using social media to engage with clients and potential clients). 2017 is also expected to see a rise in the usage of AI (Artificial Intelligence), and to be the year that E-Discovery goes mainstream. Cybercrime & Cyberwarfare, too, will remain in the news.

Let’s have a closer look at these items.

More Mobile

In 2016, for the first time worldwide, we saw more mobile devices being used online than desktops. This trend is expected to continue. More lawyers will start using mobile apps. They also will start accommodating their mobile clients – and potential clients – more. (We recently published two articles on the subject, where you can find more information).

Cloud

2017 will see a further increase in cloud usage. The could will play an increasingly important role in collaboration between lawyers. Bigger law firms are expected to start using big data analytics. The cloud will also play a significant role in the further development of AI and E-Discovery (see below).

Cybersecurity

Cybercrime will continue to rise, and will continue to become more and more sophisticated. AI will increasingly be used in cyber-protection, as well as in attacks. Experts also expect an increase in cyberwarfare.

Social Media – Business Social

More lawyers will start embracing social media, and as a result they will become more responsive, i.e. engage more with clients and potential clients. More specifically, for lawyers, an increase is expected in the usage of professional or business social media. Some experts foresee an important role for new players (service providers) on this market.

AI

In 2017, AI will continue its rise, and become more omnipresent. The main focus of artificial intelligence in legal tech will remain on Machine Learning. More specifically, AI will continue to push legal technology in the fields of Legal Research (with, e.g., virtual Legal Research assistants), Contract Review, Security, and E-Discovery (see further). One expert also expects AI to be introduced in legal practice management, as well as legal project management, which, in turn could lead to significant advances being made in those fields.

E-Discovery

Last, but not least, 2017 is the year E-discovery is expected to go mainstream. E-Discovery, also spelled eDiscovery, stands for electronic discovery. It refers to the discovery of relevant information in legal proceedings – such as litigation, government investigations, or Freedom of Information Act requests – where the information that is being analyzed is stored in an electronic format. Think, e.g., about the recent example of the FBI analyzing tens of thousands of emails that were leaked by WikiLeaks, in just four days. As more and more information is being stored electronically, E-Discovery is becoming more and more important. In 2017, it is expected to go mainstream.

Experts predict the following trends for E-Discovery in 2017:

  • The increase in social media usage implies that E-Discovery will have to be able to incorporate the analysis of social media information as well.
  • The Internet of Things will also have a serious impact on E-Discovery, as it will have to learn to process the data that are produced by billions of devices. In the US, e.g., there is a murder case where Amazon is asked to give access to the data one of its digital Echo devices (virtual assistants) may have recorded as evidence.
  • Because of these two developments (social media & Internet of Things), data privacy is becoming more important than ever.
  • Machine Learning is expected to become the most important technology for E-Discovery.
  • Cross-border compliance will continue shaping E-Discovery: multinationals, e.g., must comply with laws in several countries. This has implications on what can be stored where, which in turn has its effects on E-Discovery.

 

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AI Predicts Outcome of Human Rights Cases

It’s the stuff of science fiction: an artificial intelligence judges the merits of a court case and reaches a verdict. We are now one step closer to that being a reality. Researchers have created an artificial intelligence system that has accurately predicted the outcomes of many cases heard at the European Court of Human Rights (ECHR). Out of 584 cases, the system had a success rate of 79% where it correctly decided whether there had been a Human Rights’ violation or not.

The project was carried out by researchers at University College London and the universities of Sheffield and Pennsylvania. The method consisted of automatically analyzing case text using a machine learning algorithm. For the learning phase, equal amounts of cases with violations and without violations were used. In developing the method, the team found that judgements by the ECHR are highly correlated to non-legal facts rather than directly legal arguments, suggesting that judges of the Court are, in the jargon of legal theory, ‘realists’ rather than ‘formalists’.

The most reliable factors for predicting the court’s decision were found to be the language used, as well as the topics and ‘circumstances’ mentioned in the case text. The ‘circumstances’ section of the text includes information about the factual background to the case.

The team identified English language data sets for 584 cases related to three articles of the Convention on Human Rights:

  • Article 3: cases involving torture or degrading treatment, good for 250 cases
  • Article 6: rights to a fair trial, good for 80 cases
  • Article 8: respect for private life, good for 254 cases

By combining the information extracted from a) the abstract ‘topics’ that the cases cover and b) the ‘circumstances’ across data for all three articles, an accuracy of 79% was achieved.

The 21% of cases where the prediction was not correct involved situations where there were similar cases but with different outcomes. This could be an indication that in those cases, the finer subtleties of the law were missed by the artificial intelligence.

One of the key researchers, Dr Nikolaos Aletras, who led the study at UCL, said the following about using AI to predict cases: ‘We don’t see AI replacing judges or lawyers, but we think they’d find it useful for rapidly identifying patterns in cases that lead to certain outcomes. It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights.’

Given the huge numbers of cases that the ECHR’s legal staff has to consider every year, the program could indeed prove very useful: in 2015, e.g., the court had to process 40,650 applications for a hearing, while in 2014 it processed 56,200 cases.

It is easy to see how such programs could benefit law firms as well. Having intelligent software at your office to predict the outcome of a case would offer a clear and objective view of the strengths and weaknesses of your case and argumentation. This could then help devise a different argumentation if necessary. Alternatively, it could help avoiding the costs of litigation if similar cases were not considered ‘winnable’.

Does this mean lawyers or judges should start fearing for their jobs? No, it doesn’t. The program still misses the finer subtleties of the law. As Dr Aletras pointed out, it is meant as a tool to assist, not to replace. In the long run, though, it could result in lawyers and judges dealing with more complex or specialized cases, while the processing of simpler cases gets more automated.

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The bots are coming

In last week’s article, we mentioned the rise of virtual offices and websites that offer legal services. One of those, www.donotpay.co.uk, has drawn a lot of attention, recently. The website is the brain child of nineteen-year-old Joshua Browder, who refers to it as the world’s first robot lawyer. He created the website after he received more than thirty unfair traffic fines in London in just a few months, and wanted to appeal them. The website is designed to make that process as easy as answering a few questions, either by filling out a form, or in a chatroom. The website then files the appeal on your behalf, for free.

That Browder chose a chatroom solution that uses a chat bot shouldn’t come as a surprise. Indeed, experts have been predicting that the use of chat bots to interact with customers would constitute one of the biggest new technology shifts. After all, chatting apps are extremely popular, and offer users a familiar interface. Chances are you’ve already used a chat room to interact with IT support services or with companies’ customer services. The natural next step was that chat bots would get involved to automate and streamline the process. Tests have shown that the artificial intelligence used in chat bots has sufficiently matured to pass the Turing Test, i.e., the person interacting with the chat bot does not realise he or she is interacting with a chat bot. In the US, e.g., chat bots are already being used successfully in fast food chains to process online orders. Or you can just call an Uber through Facebook Messenger. Chat bots can also get you news headlines, weather forecasts, or traffic information. IT companies like Slack.com, e.g., already use a chat bot (called slackbot) for their online customer support. Microsoft acknowledged the potential of chat bots when it started working in March on tools that allow you to create your own chat bot on Skype.

With DoNotPay, the first legal chat bot is now a fact, too. When the website started offering Londoners an easy way to appeal unfair traffic fines for free, it did so with huge success: in less than two years’ time, 250 000 appeals were submitted and 160 000 of those were successful. As a result, approximately four million USD worth in parking tickets did not have to be paid. By now, people in New York can use the service as well. Seattle is next, and South Africa may follow as well. And that’s not all. Given its success, the website intends to extend the services it offers. Apart from appealing parking tickets, the website can now also assist you in claiming compensation if your flight was delayed.

In an interview with Fortune Magazine Catherine Bamford, a former lawyer in Leeds who advises law firms and corporate legal departments on automation, underlined how important this evolution is. “Access to justice for the non-wealthy is a serious concern. Legal aid budgets have been slashed in recent years. With helper bots like DoNotPay, some willing lawyers and expert programmers, legal advice could become cheap and accessible to everyone via the Internet. This is a real step in the right direction.”

The DoNotPay website wasn’t Joshua Browder’s first endeavour. The second-year IT student at Stanford, indeed has a nice track record already. As a thirteen-year-old he created an app for ‘Pret-a-manger,’ a sandwich chain, that became so popular that the company adopted it as its official app. He also contacted several human rights organizations offering his services for free. Some, like Freedom House (a human rights watchdog) and International Bridges for Justice accepted his offer.

We are likely to encounter more and more intelligent chat bots in the near future. And they won’t be limited to just support departments or customer services. One startup, x.ai, is already working on a virtual personal assistant, and uses the built-in chat bot to interact with people, e.g., to set up appointments, suggesting possible times, etc. It’s probably just a matter of time before law firms start using virtual legal assistants. In fact, IBM already offers a virtual legal research assistant. And that’s just the beginning. We can expect artificial intelligence aspects to be integrated in the user interfaces of legal software, replacing the more traditional wizards. And Law still is a field that deals with a lot of formalities. Robot lawyers that assist people with those, e.g., could be really useful, and could make things more transparent and accessible.

So, should lawyers be worried that legal robots will be taking over their jobs? Not really! After all, with each new technology arise new opportunities for lawyers, too. Take, e.g., liability questions: what if the robot lawyer makes a mistake, or if your virtual legal assistant sends out the wrong information? As long as there are legal conflicts, the need for lawyers will remain. (And that’s a good thing, because otherwise we would be out of job, too).

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