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Legal IT

The legal technology renaissance

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In this article, we discuss the legal technology renaissance that is occurring. We look at the recent legal technology boom, at some examples, and at the benefits. We observe what is driving this renaissance and what obstacles it has to overcome. We look at the consequences for the legal market, and at how to make it work for you.

The Recent Legal Technology Boom

In recent years, we have experienced a veritable legal technology renaissance, or legal tech boom, as some call it. A multitude of factors contributed to this. We have more computing power than ever before, with cloud computers doing the heavy lifting. We have made significant breakthroughs in artificial intelligence. The legal market has been changing dramatically and the legal technology market has followed suit. Finally, the pandemic too has been a catalyst for change. Law firms were forced to reorganize the way they work and invest in technology to be able to do so. In the process, many law firms took this as an opportunity to also invest in technology to improve their service delivery model. As of June and July 2021, we witnessed the first legal technology IPOs.

This boom is expected to continue in the next few years. In fact, some say this renaissance is only starting, as the demand for legal expertise is exploding. Gartner, e.g., made the following predictions:

  1. By 2025, legal departments will increase their spend on legal technology threefold.
  2. By 2024, legal departments will replace 20% of generalist lawyers with non-lawyer staff.
  3. By 2024, legal departments will have automated 50% of legal work related to major corporate transactions.
  4. By 2025, corporate legal departments will capture only 30% of the potential benefit of their contract life cycle management investments.
  5. By 2025, at least 25% of spending on corporate legal applications will go to non-specialist technology providers.

Big law firms and legal departments have taken the lead in this legal technology renaissance. By now, mid-size law firms and small law firms are catching up and starting to reap the benefits as well.

Some examples

Let us have a look at some examples where legal technology has changed the ways law firms and legal departments operate. A first area has to do with streamlining the administrative operations of the law firm or legal department. Examples here include document automation, E-Billing, and E-Filing. A second area has to do with streamlining casework, where progress has been made with eDiscovery software and with case management software. In both areas, far more aspects of the overall process have been automated than ever before. A third area has to do with collaboration and exchange of ideas. We are seeing a steady rise in online collaboration tools, in the use of AI-enabled chatbots and virtual legal assistants, in online education, and in video conferencing, where the pandemic resulted in a sharp increase in the available tools. Finally, major progress has also been made in the availability and usage of different kinds of analytics. These provide us with new insights in how law firms and legal departments can be run more efficiently. They also offer new insights into patterns when conducting legal research. Predictive analytics, e.g., allow to predict the chances of success in specific cases.

Benefits

The benefits of this legal technology renaissance are threefold. A first benefit is greater efficiency and better service delivery. Automation reduces errors, speeds up and improves the quality of legal service delivery. It also allows for greater scalability. A second benefit is the greater insight we gain. These are the result of Machine Learning and analytics, but also of analyzing our workflows for automation so they can be optimized. Finally, the boom in legal technology is helping to bridge the Access to Justice gap.

What drives this legal technology renaissance?

There are three key concepts that are central to this legal technology renaissance. First, it Is about automation. The technological progress that has been made allows to automate far more of the legal service delivery process. The mantra has become to automate where possible to increase productivity and efficiency. If law firms want to remain competitive, automation is inevitable.

A second aspect of this boom has to do with Legal Digital Transformation. The Global Tech Council describes it as the digitizing all areas of legal expertise, including service delivery, workflow, procedures, team communication, and client interaction in the legal sector. The Internet has changed the way we live, where we spend part of our lives online, in a digital world. With some delay, the legal sector is becoming part of that digital world, too.

Finally, the legal technology renaissance is about a new legal services delivery model that is more efficiency-driven, more client-centred, and provides all stakeholders with more insight.

Issues / Obstacles

Not everybody is reaping the benefits yet of these technological breakthroughs. Lawyers are traditionally rather conservative when it comes to their adoption of new technologies. Richard Tromans points to two main issues that are obstacles to greater adoption.

A first issue is “the belief that any of the above applications that relate to automation and improved workflows are somehow an answer in and of themselves, rather than part of a much larger integrated approach to legal services delivery.”

The other challenge stems from the fact that these technologies change the way law firms operate. It isn’t as simple as plug and play. The technologies may not meet over-elevated expectations. And the implementation of new technologies needs to be part of a bigger strategy around service delivery. In essence, these changes need an engagement from not only the IT team, but from the lawyers as well, who will need a hybrid mix of skills. Tromans warns that this can lead to disillusionment and people backing away.

Consequences on the legal market

This legal technology boom is changing the legal market. We already pointed out that it changes the way law firms and legal departments operate. As mentioned above, this technology boom is introducing new legal services delivery models that focus on being more client-centred, on increased efficiency, and increased insight.

As second consequence is the introduction of new players on the legal market. There are plenty of alternative legal service providers. Some of these offer services to legal consumers. These include, e.g., legal chatbots like DoNotPay or DivorceBot. Most of them, however, offer specialized services for law firms and legal departments. These include services like eDiscovery, document automation and review, legal research assistance, analytics, etc.

A third change has to do with the hybrid skill set that is needed in this changed service delivery model. More and more bar associations are opening up to changes in the corporate structure of firms offering legal services. Law firms are allowed to have shareholders, co-owners, and directors that are not lawyers. At the same time, corporate entities are being allowed to offer certain legal services. Some bars are even considering giving accreditation to some alternative legal service providers.

How to make the legal technology renaissance work for you

Making the legal technology renaissance work for you is not a guaranteed immediate success story. Here are some considerations that may be useful.

There are four key elements to planning your digital transformation process. The first two are the selection of 1) the best legal platform, 2) and the best IT infrastructure for that platform. This includes deciding whether to host on-site or in the cloud or opting for a hybrid solution. 3) Understand that optimizing workflows involves legal engineering. And 4) If you are going to use AI-powered solutions, you will need Machine Learning support.

When choosing your best legal platform, consider that the 2021 ABA Legal Tech Survey support observed that as a rule, most solutions work out-of-the-box, and that no customization is required. Experience has also shown that directly using the solution out-of-the-box allows to reap more benefits and faster.

Experience also demonstrated that an incremental implementation strategy tends to be more successful than a once-off big-bang transformation. Such a staged approach leads to success faster and more consistently.

Digital transformation projects tend to be more successful if the firm has some product champions, i.e., users who commit to familiarizing themselves with the solutions first. They can then assist others, show them how to reap the benefits of the new technologies, and convince others to start using them, too.

While implementing a digital transformation process, focus on business outcomes rather than on features. And set realistic ROI benchmarks.

Conclusion

The legal technology boom is disrupting the legal market for the better. As implementing these new technologies changes the way we work, some growing pains are to be expected. A balanced and staged implementation approach offers the biggest chances of success. To remain competitive in a changing market, law firms and legal departments have no choice but to adapt. Some fear that all these changes will make law firms obsolete. The experts don’t agree. Tromans points out that, while technology is very important in moving today’s legal sectors forward, there will undoubtedly always be a need for a human presence and personal connection with clients.

 

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Legal IT

Artificial Intelligence, Ethics, and Law

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Every day, artificial intelligence (AI) is becoming more entrenched in our lives. Even cheap smart phones have cameras that use AI to optimize the pictures we are taking. Try getting online assistance for a problem you are facing, and you are likely to first be met by a chatbot rather than a person. We have self-driving cars, trucks, buses, taxis, trains, etc. AI can be a force for good, but it can also be a force for bad. Cybercriminals are using AI to steal identities and corporate secrets, to gain illegal access to systems, transfer funds, avoid police detection, etc. AI is being weaponized and militarized. This raises ethical concerns, and the possibility that legal frameworks will have to be implemented to address those concerns.

Let us first touch upon some of the ethical problems we are already being confronted with. The use of facial recognition software that is being implemented in airports and big cities raises both privacy and security concerns. The same concerns pertain to using big data for Machine Learning. In previous articles, we already paid attention to the problem of bias in AI, where the AI algorithms inherit our biases because they are reflected in the data sets they use. One of the areas where the ethical issues of AI really come to the forefront is with self-driving vehicles. Let us explore that example more in depth.

Sometimes, traffic accidents cannot be avoided, and those may lead to fatalities. Imagine the brakes of your car stop functioning, while you are driving down a street. Ahead of you are some children getting out of car that is standing still, in the lane for oncoming traffic a truck is coming, and on the far side of the road some people are on the pavement talking. What do you do? And what is a self-driving car supposed to do? With self-driving cars, the car maker may have to make the decision for you.

In ethics, this problem is usually referred to as the Trolley Problem. A runaway trolley is racing down a railroad track, and you are standing at a switch that can change the track it is on. If you do not do a thing, five people will be killed. If you switch the lever, one person will be killed. What is the right thing to do?

The Moral Machine experiment is the name of an online project where different variations of the Trolley Problem were presented to people from all over the world. It asked questions to determine whether saving humans should be prioritized over animals (including pets), passengers over pedestrians, more lives over fewer, men over women, young over old, etc. It even asked whether healthy and fit people should be prioritized over sick ones, people with a high social status over people with a low social status, or law-abiding citizens over ones with criminal records. Rather than posing the questions directly the survey typically would present people with combined options: kill three elderly pedestrians or three youthful passengers?

Overall, the experiment gathered 40 million decisions in 10 languages from millions of people in 233 countries and territories. Surprisingly, the results tended to vary greatly from country to country, from culture to culture and along lines of economics. “For example, participants from collectivist cultures like China and Japan are less likely to spare the young over the old—perhaps, the researchers hypothesized, because of a greater emphasis on respecting the elderly. Similarly, participants from poorer countries with weaker institutions are more tolerant of jaywalkers versus pedestrians who cross legally. And participants from countries with a high level of economic inequality show greater gaps between the treatment of individuals with high and low social status.” (Karen Hao, in Technology Review)

In general, people agreed across the world that sparing the lives of humans over animals should take priority, and that many people should be saved rather than few. In most countries, people also thought the young should be preserved over the elderly, but as mentioned above, that was not the case in the Far East.

Now, this of course raises some serious questions. Who is going to make those decisions and what will they be choosing, considering these different choices people suggested? Are we going to have different priorities depending on whether we are using e.g. Japanese or German self-driving cars? Or will the car makers have the car make different choices based on where the car is driving? And what if more lives can be spared if we sacrifice the driver?

When it comes to sacrificing the driver, one car manufacturer, Mercedes, has already made clear that will never be an option. The justification they give, is that self-driving cars will lead to far fewer accidents and fatalities, and that those occasions where pedestrians are sacrificed for drivers will be cases of acceptable collateral damage. But is that the right choice, and is it really up to the car maker to make that choice?

An ethicist identified four chief concerns that must be addressed when we look for solutions with regard to ethical AI:

  1. Whose moral standards should be used?
  2. Can machines converse about moral issues? (What if e.g. multiple self-driving vehicles are involved? Will they communicate with each other to choose the best scenario?)
  3. Can algorithms take context into account?
  4. Who should be accountable?

Based on these considerations, some principles can be established to regulate the use of AI. In a previous article we already mentioned the principles the EU and OECD suggest. In 2018, the World Economic Forum also had already suggested 5 core principles to keep AI ethical:

  • AI must be a force for good and diversity
  • Intelligibility and fairness
  • Data protection
  • Flourishing alongside AI
  • Confronting the power to destroy

An initiative that involves several tech companies also identified seven critical points:

  1. Invite ethics experts that reflect the diversity of the world
  2. Include people who might be negatively impacted by AI
  3. Get board-level involvement
  4. Recruit an employee representative
  5. Select an external leader
  6. Schedule enough time to meet and deliberate
  7. Commit to transparency

A deeper question, however, is whether the regulation of AI should really be left to the industry? Shouldn’t these decisions rather be made by governments? The people behind the Moral Machine experiment think so, as do many scientists and experts in ethics. Thus far, however, not much has been done when it comes to legal solutions. At present, there are no legal frameworks in place. The best we have is for members of the EU and the OECD who have put some guidelines in place, but those are merely guidelines that are not enforceable. And that is not enough. A watchdog organization in the UK warned that AI is progressing so fast that we already are having difficulties catching up. We cannot afford postponing addressing these issues any longer.

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Legal IT

A Chatbot For Your Law Firm

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We have talked about chatbots on several occasions in the past. Most of those targeted legal consumers. Today, we’ll have a look at how a chatbot could benefit your law firm.

Let’s start by defining what a chatbot is. A chatbot is a computer program designed to mimic human conversation. It is typically powered by rules or by more advanced Artificial Intelligence technologies like Machine Learning. Most chatbots are text-based, but more advanced ones like Siri or Alexa, are voice-based. In law firms, they are often used for simple tasks like increasing lead generation, client intake, booking an appointment or accepting payments. More advanced legal chatbots can generate, review and analyse legal documents.

You may think a chatbot is not for your law firm, but you’d be mistaken. There are many benefits, both for your clients and prospective clients, as well as for your law firm.

What makes chatbots attractive to the legal consumers?

  • First, there is the unprecedented popularity of messaging apps. One of the reasons chatbots can be found everywhere is because they became popular on messaging aps. The first chatbots appeared on Facebook Messenger and soon after were offered on other platforms like Skype, weChat, Telegram, Slack, Kik, Line, and SMS.
  • People love their mobile devices, and chatbots are typically designed for mobile first.
  • People love to text. Did you know text messages boast a 98% open rate? Chatbots benefit from this.
  • People love interaction, and chatbots are interactive. They increase engagement.
  • Chatbots are available 24/7.
  • Our online culture is an instant gratification culture. Chatbots can give instantaneous responses. Research shows that 70 % of consumers prefer a chatbot to interacting with a human being, if it means they’ll get an instantaneous response.
  • Chatbots can mimic lawyers for several tasks, which means the legal consumers who need those services can get their needs met faster, and typically at a lower or no cost.

What are the benefits for your law firm?

  • Because consumers love interaction, conversational marketing has become a key part of promotion for any business, including law firms.
  • Chatbots can perform repetitive tasks that lawyers do. They have proven useful in:
    • Client acquisition and intake, as well as lead generation.
    • Answering FAQs, so you don’t have to email back and forth answering questions you are frequently asked.
    • Document generation and review.
  • Using chatbots to take care of repetitive tasks therefore leaves you more time for more productive and profitable endeavours.

So how do you get started? Once you know what you want your chatbot to do, there are plenty of tools available. In his article, “5 Often-Overlooked Steps to Building a Useful Chatbot for Your Law Practice“, Tom Martin from Lawdroid explains the best way to proceed. He outlines 5 steps.

Step 1 is to determine what your chatbot’s purpose is. Do you, e.g., want to use it to allow new clients to enter their details into your system and book an appointment? Or do you want a more advanced bot who, e.g., can generate or analyse legal documents? Be as specific as possible.

Step 2 is to determine where your bot lives. Will you offer your chatbot on your website, or on Facebook, or Whatsapp, etc.?

In step 3, you choose your bot’s personality: its name, visual style, backstory, and the conversational tone. (People enjoy a bit of humour). Make sure you also tell people they are dealing with a chatbot.

Step 4 is to determine your chatbot’s conversation structure.  Martin breaks this down in six components. First, you need to do some preparation where you look at some essential questions like who your target audience is (e.g. existing or new clients), what they are trying to do, and what they need for that. Next, you can diagram your dialog tree, where you map how the conversation can unfold. Let your chatbot start the conversation with a greeting, and make sure that you manage the users’ expectations: explain what the bot can and cannot do.  Martin calls the next step the “Glide Path to Goal”: the conversation should lead the user to a goal, and to reach that goal as effectively as possible, open-ended questions should be avoided. So, it’s good to suggest possible answers the user can choose from. Once the conversation is ended, and the goal is achieved, it’s good to thank the user, and to provide him or her with a deliverable or a specific call to action. Last but not least, make sure you pay sufficient attention to error handling.

In the fifth and last step, you choose what tools you will use to build your bot. Martin’s article includes a checklist and a list of available platforms.

The checklist includes the following items:

  • Is creating the chatbot free or paid?
  • Is any coding required?
  • What are the publishing platforms for the chatbot?
  • Does it use or need Artificial intelligence?
  • How are the third-party integrations with apps like Gmail, MailChimp, Office 365, etc.
  • What are the supported languages?
  • What is the recommended use? (You don’t need a bot, e.g., that uses Machine Learning if you only want your new clients to fill out their details).

If you want to build your legal chatbot, the following platforms are currently available:

(In his article, Martin goes over the checklist items for each of these platforms).

Let’s leave it at that for now. We’ve only been able to scratch the surface of this topic. The articles listed below can help you further.

 

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Legal IT

Legal AI is still biased in 2019

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In October 2017, we published an article on how legal Artificial Intelligence systems had turned out to be as biased as we are. One of the cases that had made headlines was the COMPAS system, which is risk assessment software that is used to predict the likelihood of somebody being repeat offender. It turned out the system had a double racial bias, one in favour of white defendants, and one against black defendants.

To this day, the problems persist. By now, other cases have come to light. Similar to the problems with the COMPAS system, e.g., algorithms used in Kentucky for cash bail applications consistently preferred white defendants. The situation is similar in the UK, where a committee concluded that bias and inaccuracy render artificial intelligence (AI) algorithmic criminal justice tools unsuitable for assessing risk when making decisions on whether to imprison people or release them. Algorithmic bias was also discovered in systems to rank teachers, and for natural language processing. In the latter, there was a racial bias with regard to hate speech, as well as a gender bias in general.

To research and address the problems with Artificial Intelligence, the ‘AI Now Institute’ was created.  Bias is one of the four areas they specifically focus on. They found that bias may exist in all sorts of services and products. A key challenge we face in addressing the problems is that “crucial stakeholders, including the companies that develop and apply machine learning systems and government regulators, show little interest in monitoring and limiting algorithmic bias. Financial and technology companies use all sorts of mathematical models and aren’t transparent about how they operate.”

So, what is algorithmic bias? The Wikipedia defines it as “systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. Bias can emerge due to many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm.”

The AI Now Institute clarifies that artificial intelligence systems learn from data sets, and that those data sets reflect the social, historical and political conditions in which they were created. As such, they reflect existing biases.

It may be useful to make a distinction between different types of algorithmic bias. Eight different types have been identified thus far:

  1. Sample bias is the most common form of bias. It is when the samples used for the data sets are themselves contaminated with existing biases. The examples given above are all cases of sample bias.
  2. Prejudice bias is one of the causes of sample bias. Prejudice occurs as a result of cultural stereotypes in the people involved in the process. A good example of this are the New York Police Department’s stop and frisk practices. In approximately 83 percent of the cases, the person who was stopped was either African American or Hispanic, where both groups combined only make up just over half of the population. An AI system that learns form a data set like that will inherit the human racial bias that thinks people are more likely suspicious if they’re African American or Hispanic. So, because of prejudice, factors like social class, race, nationality, religion, and gender can creep into the model, and completely skew the results.
  3. Confirmation bias is another possible cause for sample bias. Confirmation bias is the tendency to give preference to information that confirm one’s existing beliefs. If AI systems are used to confirm certain hypotheses, the people selecting the data may – even subconsciously – be inclined to select the data in function of the hypothesis they’re trying to prove.
  4. Group Attribution Bias is the type of bias where the data set contains an asymmetric view of a certain group. An example for that was Amazon’s AI assistant for the Human Resources department. Because Amazon had far more male engineers working for them than female engineers, the system concluded that male engineers had to be given preference over female engineers.
  5. The Square Peg Bias has to do with selecting a data set that is not representative and is chosen because it just happens to be available. It is also known as the availability bias.
  6. The Bias-variance Trade-off. This is a bias that is introduced to the system by mathematically over-correcting for variance. (An example to clarify: Say you have a data set where 30% of the people involved are female. Therefore, females are effectively underrepresented in your data set. To compensate you use mathematical formulas to ‘correct’ the results). This mathematical correction can introduce new biases, especially in more complex data sets, where the corrections could lead to missing certain complexities.
  7. Measurement Bias has to do with technical flaws that contaminate the data set. Say you want to weigh people and use scales, but they’re not measuring correctly.
  8. Stereotype Bias. The example given above with Amazon also qualifies as a gender stereotype bias. There are more male engineers than female engineers. That may lead systems to favour male engineers, and/or to preserve the ratio existing in the data set.

The good news is that as we are getting better at understanding and identifying the different types of algorithmic bias, we also are getting better at finding solutions to counteract them.

 

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Legal IT

Legal Bots in 2019

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It has been two years since we published our article with an overview of legal bots. Since then, a lot has happened, and on several occasions legal bots made headlines: We have dedicated articles, e.g., to legal bots beating lawyers at specific tasks, and to the rise of robot clerks, prosecutors and judges. Overall, we have witnessed an unprecedented proliferation of digital assistants who are transforming public service and legal service delivery. We now have bots who offer services for legal consumers, as well as for the various legal professions: lawyers, prosecutors, judges, notaries, and paralegals.

By now, there are so many different legal bots that it is no longer possible to mention all of them within the scope of one blog article. In fact, it would probably be possible to dedicate entire articles to each individual bot. So, we will have a look at how the ones we discussed two years ago are doing, what new players have followed their examples, and at some of the more interesting recent arrivals on the scene.

Back in July 2017, DoNotPay already was the most impressive legal bot. What started as a simple bot to appeal traffic tickets, evolved into a system that assists legal consumers in the UK, the US, and Canada, on a wide range of topics, including seeking asylum, claiming damages from airlines, filing harassment claims at work, etc. Since then it has increased the services it offers, and now also assists, e.g., with divorces. More importantly, DoNotPay has become a platform that can assist you in creating your own legal bots. Early July 2019, Joshua Browder announced DoNotPay had raised 4.6 million USD in seed funding. So, we can expect it to continue being an important player in the market.

Lawdroid started off as an intelligent legal chatbot that assisted entrepreneurs in the US in incorporating their business. Soon after, Lawdroid became a platform to create bots, as it began to create legal chatbots on behalf of lawyers. Since then, it has further expanded its services, and, e.g., now also offers its own divorce bot, called Larissa.

The examples of DoNotPay and Lawdroid were followed by others who now, too, are offering a platform to create legal bots. Worth mentioning are Josef and Automio, and even Facebook. Any lawyer can create a legal chatbot on Facebook Messenger. Getting started is as easy as buying and customizing commercial templates that are available from as little as 50 USD.

Billybot was the first legal clerk that assisted people in finding a lawyer near them to assist them. Its example was widely followed. In a previous article, we mentioned Victor, the clerk the Flemish Order of Bar Associations has created.

In the last 2 years, Lawbot in the UK first changed its name to Elixirr and then to CaseCrunch. They expanded the range of bots they have been offering, as well as the countries in which those bots are available. They made headlines when their Case Cruncher Alpha competed with over 100 lawyers in predicting the outcomes of cases and won. Similarly, LawGeex was better at evaluating Non-Disclosure Agreements than its human counterparts. By now, there are more and more bots available that try and predict the outcomes of cases. One of them that focuses on issues relating to landlord-tenant disputes, e.g., is Procezeus.

Lawbot probably also was the first to offer a divorce bot. That example, too, got many followers. We already mentioned that both Lawdroid and DoNotPay now also offer divorce bots. Two other ones worth mentioning are the divorce bot on Reddit, and Hello Divorce by Erin Levine, which streamlines and automates the process of divorces in California to the point that in most cases no intervention from lawyers is needed.

Lawbot also offered a legal research assistant, called Denninx. By now, many legal research assistants are available. Best known are IBM’s Ross and Eve. Most legal publishers, too, are providing digital assistants to help with legal research.

Below follows a random selection of other bots that were discussed in the literature.

  • In the US, Coralie is a virtual assistant that helps survivors of military sexual trauma connect with services and resources. It has won the Tech for Justice hackathonduring the American Bar Association’s Techshow.
  • Docubot is a chatbot that can be integrated in lawyers’ websites to help consumers generate legal documents. It also assists the lawyers with client intake through their website.
  • Another bot using the name LawBot comes from the Indian company LawRato. It helps users get answers to legal questions and recommendations of a lawyer.
  • Legalibotin Spain helps users compose legal documents and contracts through Facebook Messenger.
  • In Australia, Leximade headlines. This bot can be used to generate free privacy policy documents or non-disclosure agreements. It asks questions and uses the responses to give general information and create a document with the relevant details.
  • Also in Australia, Speak with Scout is a chatbot that works through Facebook Messenger to provide legal guidance as well as references to a lawyer.
  • Still in Australia, Parker is a chatbot that uses natural language processing and IBM’s Watson platform to answer users’ questions about data breaches and privacy law.
  • In the UK, RentersUnionis a chatbot that provides legal advice on housing issues for residents of London. The bot analyses a user’s tenancy agreement and then helps generate letters or recommends appropriate action.
  • In the US, Visabot is a legal chatbot that can assist with multiple immigration issues.
  • Also in the US, and more specifically in Utah, Solosuit is a chatbot/expert system that handles debt law. It asks for all the relevant information it needs, and then fills out the appropriate legal document.

 

Also worth mentioning is that several bar associations are considering officially recognizing / approving certain bots that offer legal services. That way, legal consumers can have some reassurance that the advice they are getting is trustworthy.

 

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Uncategorized

International Guidelines for Ethical AI

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In the last two months, i.e. in April and May 2019, both the EU Commission and the OECD published guidelines for trustworthy and ethical Artificial Intelligence (AI). In both cases, these are only guidelines and, as such, are not legally binding. Both sets of guidelines were compiled by experts in the field. Let’s have a closer look.

“Why do we need guidelines for trustworthy, ethical AI?” you may ask. Over the last years, there have been multiple calls, from experts, researchers, lawmakers and the judiciary to develop some kind of legal framework or guidelines for ethical AI.  Several cases have been in the news where the ethics of AI systems came into question. One of the problem areas is bias with regard to gender or race, etc. There was, e.g., the case of COMPAS, which is risk assessment software that is used to predict the likelihood of somebody being repeat offender. It turned out the system had a double racial bias, one in favour of white defendants, and one against black defendants. More recently, Amazon shelved its AI HR assistant because it systematically favoured male applicants. Another problem area is privacy, where there are concerns about deep learning / machine learning, and with technologies like, e.g., facial recognition.

In the case of the EU guidelines, another factor is at play as well. Both the US and China have a substantial lead over the EU when it comes to AI technologies. The EU saw its niche in trustworthy and ethical AI.

EU Guidelines

The EU guidelines were published by the EU Commission on 8 April 2019. (Before that, in December 2018, the European Parliament had already published a report in which it asked for a legal framework or guidelines for AI. The EU Parliament suggested AI systems should be broadly designed in accordance with The Three Laws of Robotics). The Commission stated that trustworthy AI should be:

  • lawful, i.e. respecting all applicable laws and regulations,
  • ethical, i.e. respecting ethical principles and values, and
  • robust, both from a technical perspective while taking into account its social environment.

To that end, the guidelines put forward a set of 7 key requirements:

  • Human agency and oversight: AI systems should empower human beings, allowing them to make informed decisions and fostering their fundamental rights. At the same time, proper oversight mechanisms need to be ensured, which can be achieved through human-in-the-loop, human-on-the-loop, and human-in-command approaches
  • Technical Robustness and safety: AI systems need to be resilient and secure. They need to be safe, ensuring a fall-back plan in case something goes wrong, as well as being accurate, reliable and reproducible. That is the only way to ensure that also unintentional harm can be minimized and prevented.
  • Privacy and data governance: besides ensuring full respect for privacy and data protection, adequate data governance mechanisms must also be ensured, taking into account the quality and integrity of the data, and ensuring legitimised access to data.
  • Transparency: the data, system and AI business models should be transparent. Traceability mechanisms can help achieving this. Moreover, AI systems and their decisions should be explained in a manner adapted to the stakeholder concerned. Humans need to be aware that they are interacting with an AI system, and must be informed of the system’s capabilities and limitations.
  • Diversity, non-discrimination and fairness: Unfair bias must be avoided, as it could have multiple negative implications, from the marginalization of vulnerable groups, to the exacerbation of prejudice and discrimination. Fostering diversity, AI systems should be accessible to all, regardless of any disability, and involve relevant stakeholders throughout their entire life circle.
  • Societal and environmental well-being: AI systems should benefit all human beings, including future generations. It must hence be ensured that they are sustainable and environmentally friendly. Moreover, they should consider the environment, including other living beings, and their social and societal impact should be carefully considered.
  • Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes. Auditability, which enables the assessment of algorithms, data and design processes plays a key role therein, especially in critical applications. Moreover, adequate an accessible redress should be ensured.

A pilot project will be launched later this year, involving the main stakeholders. It will review the proposal more thoroughly and provide feedback, upon which the guidelines can be finetuned. The EU also invites interested business to join the European AI Alliance.

OECD

The OECD consists of 36 members, approximately half of which are EU members. Non-EU members include the US, Japan, Australia, New Zealand, South-Korea, Mexico and others. On 22 May 2019, the OECD Member Countries adopted the OECD Council Recommendation on Artificial Intelligence. As is the case with the EU guidelines, these are recommendations that are not legally binding.

The OECD Recommendation identifies five complementary values-based principles for the responsible stewardship of trustworthy AI:

  1. AI should benefit people and the planet by driving inclusive growth, sustainable development and well-being.
  2. AI systems should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards – for example, enabling human intervention where necessary – to ensure a fair and just society.
  3. There should be transparency and responsible disclosure around AI systems to ensure that people understand AI-based outcomes and can challenge them.
  4. AI systems must function in a robust, secure and safe way throughout their life cycles and potential risks should be continually assessed and managed.
  5. Organisations and individuals developing, deploying or operating AI systems should be held accountable for their proper functioning in line with the above principles.

Consistent with these value-based principles, the OECD also provides five recommendations to governments:

  1. Facilitate public and private investment in research & development to spur innovation in trustworthy AI.
  2. Foster accessible AI ecosystems with digital infrastructure and technologies and mechanisms to share data and knowledge.
  3. Ensure a policy environment that will open the way to deployment of trustworthy AI systems.
  4. Empower people with the skills for AI and support workers for a fair transition.
  5. Co-operate across borders and sectors to progress on responsible stewardship of trustworthy AI.

As you can see, many of the fundamental principles are similar in both sets of guidelines. And, as mentioned before, these EU and OECD guidelines are merely recommendations that are not legally binding. As far as the EU is concerned, at some point in the future, it may push through actual legislation that is based on these principles. The US has already announced it will adhere to the OECD recommendations.

 

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Legal IT

A chatbot, a robot prosecutor and a robot judge

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No, this is not the first line of a joke about three robots that walked into a bar. It refers to three items that were in the news recently. We already were familiar with chatbots and robot lawyers. Now the Order of Flemish Bar Associations have launched their own chatbot; San Francisco is running a pilot project with a robot district attorney; and Estonia plans a robot judge to handle small damages claims. Let’s have a closer look at each.

The chatbot of the ‘Orde van Vlaamse Balies’ (Order of Flemish Bar Associations)

On 10 April 2019, the ‘Orde van Vlaamse Balies’ announced the launch of its new chatbot, called Victor. The initiative was taken by some bar associations, and the chatbot is meant to facilitate access to legal assistance. It does this in two ways. On the one hand, like its British counterpart Billybot, Victor helps you find a lawyer. He asks some questions to determine what area of practice your legal issue relates to. He then suggests some nearby specialist lawyers, based on the topic and the region you live in.

But Victor does more than that. The chatbot can also check whether you are eligible for a pro bono lawyer or for other types of legal assistance like reduced fees. He will ask the relevant questions, and if you are eligible, he will let you know what documents are required. If you have further questions he can’t answer, Victor will give you the contact details of the bar association that can provide you with additional answers.

Victor can be found at www.advocaat.be, as well as on the sites of the bar associations that were involved in its development: www.baliewestvlaanderen.be, www.balieprovincieantwerpen.be, and www.balielimburg.be. Victor is only available in Dutch.

The Robot District Attorney in San Francisco

About a year ago, in May 2018, the office of the District Attorney in San Francisco decided to launch a pilot project to clear convictions using algorithmic justice. Let’s give some background information first. In November 2016, recreational use of marijuana was legalized in California. For decades before the legalization of marijuana, thousands of people had received convictions for marijuana use. And now that it had become legal, the idea was to clear those preexisting convictions, and to use an algorithm to determine which cases were eligible for record clearance. As such, the algorithm is a triage algorithm. Once it determines a case is eligible, it automatically fills out the required forms. The San Francisco District Attorney then files the motion with the court.

Since the pilot project started, it has reviewed 43 years of eligible convictions. This has led to 3 038 marijuana misdemeanors being dismissed and sealed, and to recalling and re-sentencing up to 4 940 other felony marijuana convictions.

Given the success of the project, the plan is now to expand it, to eventually clear around 250 000 convictions.

The Robot Judge in Estonia

Finally, inspired by the success of the DoNotPay chatbot that offers free legal assistance in 1 000 legal areas, the Estonian government decided some weeks ago to create its own robot judge. The robot judge is meant to adjudicate small claims disputes of less than €7 000. Officials hope that the system would help clear a backlog of cases for judges and court clerks. At present the project is still in the earliest stages, but a pilot project that deals with contract disputes is scheduled for launch later this year. Parties are expected to upload the relevant information and documents, which the system will then analyze and come to a verdict. Parties will be given the option to appeal to a human judge. AI systems have been used before to assist in the triage of cases and to assist judges in their decision-making process. An autonomous robot judge, however, is a first.

So, we now have online courts, robot lawyers, prosecutors and judges. The idea that we might one day have cases handled without intervention of human lawyers suddenly has become a lot more real.

 

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Legal IT

An introduction to smart contracts

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In a previous article, we have written about Artificial Intelligence (AI) and contracts. AI is having an impact in three areas when it comes to contracts: 1. contract review, 2. contract management and automation, and 3. smart contracts. While smart contracts are automated contracts, what sets them apart from other automated contracts is the usage of Blockchain technology.

What are smart contracts? We’ll combine elements from the definitions Tech Republic and the Investopedia to explain: A smart contract is a software-based contract between a buyer and a seller. The software automates the business processes and the conditions of fulfilment contained within the contract. The code programmed into the contract actually makes the contract self-executing so that it takes action whenever a specific condition is triggered within the contract. The code and the agreements contained therein exist across a distributed, decentralized Blockchain network. Smart contracts permit trusted transactions and agreements to be carried out among disparate, anonymous parties without the need for a central authority, legal system, or external enforcement mechanism. They render transactions traceable, transparent, and irreversible. Because the smart contract is software capable of automating business processes and contract fulfilment automatically, it eliminates the need for managers and middlemen supervision.

Let’s give an example: A is a supplier of products for B. Every month, B places an order with A. It makes sense to automate this process. The smart contract is a piece of software that, e.g., would contain the code that says if an order is received by A from B, and B is not in arrears, then that order must be executed. Now, with smart contracts these transactions are typically registered in a distributed, decentralized Blockchain network of ledgers. In a previous article we explained that Blockchain is a technology that registers transactions in a ledger, where everybody in the network has a copy of that ledger. Transactions are secured by using a verification code that is calculated based on all previous transactions in the ledger. In essence, to forge a transaction, one would therefore have to forge all registrations of all transactions in all ledgers.

The benefits of smart contracts are clear: the whole process of transactions between parties can be automated, and by using Blockchain technology one has virtually irrefutable proof of the transactions. Add to that that programming code tends to be less ambiguous than the generic legalese of traditional contracts, so the chances of disputes about the interpretation of smart contracts are smaller.

The usage of smart contracts is expected to grow fast. A survey published in Forbes Magazine predicts that by 2022, 25% of all companies will be using them. Basically in any market where Blockchain technology is useful, one can expect smart contracts to be useful, too.  Smart contracts can also be the perfect complement to E.D.I. At present, smart contract applications are already being used in – or developed for – supply chains and logistics, in finance and securities, real estate, management and operations, healthcare, insurance, etc.

Still, one has to be aware of the limitations of smart contracts, as there are a number of legal issues to take into account. The name ‘smart contracts’ is misleading in that they aren’t really contracts but software. As such, there are legal concerns with regard to:

  • Offer and acceptance: is there even a binding contract, if there is no human interaction or supervision, and the transaction is completely executed automatically?
  • The evidentiary value: smart contracts are not written evidence of agreed rights and obligations because they encapsulate only a portion of any rights and obligations that is related to contractual performance
  • Jurisdiction: is the area of jurisdiction clearly defined in case of a conflict or dispute?
  • Dispute Resolution: are there any dispute resolution mechanisms in place?

When considering working with smart contracts, it is therefore a good idea to first come to a framework agreement in which these issues are addressed. And those will preferably still be written by lawyers.

 

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Legal IT

Machine Learning Applications for Lawyers

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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.

 

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Legal IT

Robot Law

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A few months ago, in January 2018, the European Parliament’s Legal Affairs Committee approved a report that outlines a possible legal framework to regulate the interactions between a) humans, and b) robots and Artificial Intelligence systems. The report is quite revolutionary. It proposes, e.g., giving certain types of robots and AI systems personhood, as “electronic persons”: These electronic persons would have rights and obligations, and the report suggests that they should obey Isaac Asimov’s Laws of Robotics. The report also advises that the manufacturers of robots and AI systems should build in a ‘kill switch’ to be able to deactivate them. Another recommendation is that a European Agency for Robotics and AI be established that would be capable of responding to new opportunities and challenges arising from technological advancements in robotics.

The EU is not alone in its desire to regulate AI: similar (though less far reaching) reports were published in Japan and in the UK. These different initiatives are in effect the first attempts at creating Robot Law.

So, what is Robot Law? On the blog of the Michalsons Law Firm, Emma Smith describes Robot Law as covering “a whole variety of issues regarding robots including robotics, AI, driverless cars and drones. It also impacts on many human rights including dignity and privacy.” It deals with the rights and obligations of AI systems, manufacturers, consumers, and the public at large in its relationship to AI and how it is being developed and used. As such, it is different from, and far broader than Asimov’s Laws of Robotics which only apply to laws robots have to obey.

Why would we need Robot Law? For a number of reasons. AI has become an important contributing factor to the transformation of society, and that transformation is happening extremely fast. The AI Revolution is often compared to the Industrial Revolution, but that comparison is partly flawed, because of the speed, scale and pervasiveness of the AI Revolution. Some reports claim that the AI Revolution is happening up to 300 times faster than the Industrial Revolution. This partly has to do with the fact AI is already being used everywhere, and that pervasiveness is only expected to increase rapidly. Think, e.g., of the Internet of Things, where everything is connected to the Internet, and massive amounts of data are being mined.

The usage of AI already raises legal issues of control, privacy, and liability. Further down the line we will be confronted with issues of personhood and Laws of Robotics. But AI also has wide-reaching societal effects. Think, e.g., of the job market and the skill sets that are in demand. These will change dramatically. In the US alone, driverless cars and trucks, e.g., will see a minimum of 3 million drivers lose their jobs. So, yes, there is a need for Robot Law.

Separate from the question of whether we need Robot Law, is the question whether we already need legislation now, and/or how much should be regulated at this stage. When trying to answer that question, we are met with almost diametrically opposing views.

The Nay-sayers claim that it is still too soon to start thinking about Robot Law. The argument is that AI and Robotics are still in their infancy, and at this stage there is a need first to explore and develop it further. Not only are there still too many unanswered questions, but their view is that regulation at this stage could stifle the progress of AI. All we would have to do, is adapt existing laws. In that context, Roger Bickerstaff, e.g., speaks of:

  • Facilitative changes – these are changes to law that are needed to enable the use of AI.
  • Controlling changes – these are changes to law and new laws that may be needed to manage the introduction and scope of operation of robotics and artificial intelligence.
  • Speculative changes – these are the changes to the legal environment that may be needed as robotics and AI start to approach the same level of capacity and capability as human intelligence – what is often referred to as the singularity point.

Others, like the authors of the aforementioned reports, disagree. They argue that there already are issues of privacy, control, and liability. There also is the problem of transparency: how do Neural Networks come to their conclusions, e.g., when they recommend whether somebody is eligible for parole, or a loan, or when they assess risks, e.g., for insurances. How does one effectively appeal against such decisions if it’s not known how the AI system reaches its conclusions? Furthermore, the speed, scale and pervasiveness of the AI Revolution and its societal effects, demand a proactive approach. If we don’t act now, we will soon be faced with problems that we know will arise.

Finally, in his paper, Ryan Calo points out, maybe surprisingly, that there already is over half a century of case law with regard to robots. These cases deal with both robots as objects and robots as subjects. He rightfully points out that “robots tend to blur the lines between person and instrument”. A second, and more alarming insight of his study was “that judges may have a problematically narrow conception of what a robot is”. For that reason alone, it would already be worthwhile to start thinking about Robot Law.

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