Henrique Costa
Legal professionals consume several types of legal information, two of which are the main ones: law and jurisprudence. The law is an abstract norm, that is, it has not been applied to a concrete case. Jurisprudence, on the other hand, is a concrete rule, made to solve a case submitted to the Judiciary.
Although it is relatively easy to know the laws, as they are published in official repositories, it is much more complex to know the jurisprudence. The most widely used legislative repository is that of the Plateau and it illustrates well how the various forms of federal legislation are organized and consumed in Brazil. In contrast, There are several courts and each one is responsible for publishing its own jurisprudence .
In general, courts treat such data as natural language documents, with a relatively limited additional layer of metadata.
Thus, there are few filters to access this information, for example: the date of the judgment, the name of the judge, the body to which this judge belongs, the name and position of each party in the process, etc. We did not, however, find any public repository organized around the dimension of the result of the judgment, whether favorable or unfavorable its outcome.
Let's consider the following use case:
It is possible to imagine that a lawyer from a bank does a research on case law in a certain court to assess the chance of success of a new lawsuit.
As the STF's judgment base is indexed, it can, with some ease, find concrete cases that dealt with a certain topic. However, the lawyer has a lot of difficulty in finding, within this topic, which were the cases won by banks and in which the same banks were defeated.
The usefulness of developing a solution that understands which are the favorable and unfavorable cases lies in enabling an aggregate consultation also by this dimension, referring to the result of the judgment. After all, the professional consultation almost always has an interested side, in such a way that knowing the outcome of the case is very important information for the practical life of legal professionals.
In the coming weeks, we will publish here the journey of several of DireitoTec's researchers, dedicated to mapping tens of thousands of STF judgments. This will make it possible to create a foundation for artificial intelligence training in such a way that it is possible to automatically classify the outcome of a judgment. What about? Sounds promising?
This post is part of a series. See the Next post .
To deal with such a complicated subject, I would like to start with a very simple notion: a chain is only as strong as its weakest link. So, if you are looking to build (or join) a legal data science laboratory within your university, you need to investigate how strong each of the following steps is:
- You have Previous research that have already made it possible to understand the legal context of a delimited field? In other words, do you already master the area of the business?
- Your previous search has been exhausted or was limited by the absence of data? In other words, is it only possible to reach a new scientific level after exploring this horizon?
- After confirming the limitations of the previous step, you have formulated problems whose answers can be obtained from data?
- In addition to the problems, you have already formulated Chance testable with this data?
- It is possible to obtain the data Demanded by his hypothesis? Is this data available at least in an unstructured way?
- If necessary, you have Structuring conditions these data?
- Once you have structured the data, you will be able to Keep updating and Evolve in modeling of the data? In other words, how disposable is your research?
- In addition to you, there is already a team with Data culture who can understand the challenges of this type of research and is willing to move in this direction?
- Your team has a Work routine and Knowledge Management that allow relatively simple individual plans to be carried out in parallel (for example, some TCC), guided by milestones that support more sophisticated research in the future (for example, a doctoral thesis)?
- Have you ever documented a Training cycle minimum to embark new researchers? Are there more cost-effective alternatives to a training cycle that depends on you? For example, is there already a data science training course that is regularly offered and accessible to potential team members?
- In addition to your subordinates, you have people with knowledge of other areas who are able to confirm viability of his ambition?
- That is, with the objective of conducting empirical research in law (applied social science), you have a Network to evolve in partnership with knowledge of technological support (exact sciences)?
- Are you open to accepting and guide your planning From this feasibility analysis, combining immediately viable research projects and a horizon of innovation to be explored?
- The results of the research can be incorporated into products that have value for the market ? You already have a plan to have Market access ?
Of course, this is not a single path. There are several types of laboratories, especially when it comes to the university context, in which a large part of the resources of the laboratories are demands for teaching or basic research activities. But if you're involved in building a laboratory that has a legal purpose and works with data, you may want to take certain precautions. After all, technology is not his main area.
In conclusion, Building a laboratory is not the same as buying equipment . A laboratory is built around problems to be solved. And these are not small problems, as they require collaboration from different areas to be overcome. The work environment and culture of this group of people are the foundations of the laboratory. In fact, it is something quite intangible.
In a world in which technological infrastructure has started to be consumed as a service (cloud computing), having physical resources is no longer an absolute competitive advantage. The real challenge is to develop a work that reconciles research and innovation with the urgency and pragmatism demanded by the market.
After all, in this area, without the market, there is no funded research. And, without money, the other conditions to create and maintain a laboratory of this type will not be present. My recommendation is that you don't go shopping on the first day, because first you need to answer the list of questions listed at the beginning of the post.
PS: While writing the post, I learned that the CNJ, by Ordinance 25/19, created a laboratory (called Inova PJe) and an Artificial Intelligence Center. I don't think the reflections in the post are fully applicable to institutional laboratories. In fact, I see the CNJ more as a decision-making body than an operational one. The operation itself would take place, for example, in an agreement with an academic laboratory, whose operation I described in the post.
To deal with such a complicated subject, I would like to start with a very simple notion: a chain is only as strong as its weakest link. So, if you are looking to build (or join) a legal data science laboratory within your university, you need to investigate how strong each of the following steps is:
- You have Previous research that have already made it possible to understand the legal context of a delimited field? In other words, do you already master the area of the business?
- Your previous search has been exhausted or was limited by the absence of data? In other words, is it only possible to reach a new scientific level after exploring this horizon?
- After confirming the limitations of the previous step, you have formulated problems whose answers can be obtained from data?
- In addition to the problems, you have already formulated Chance testable with this data?
- It is possible to obtain the data Demanded by his hypothesis? Is this data available at least in an unstructured way?
- If necessary, you have Structuring conditions these data?
- Once you have structured the data, you will be able to Keep updating and Evolve in modeling of the data? In other words, how disposable is your research?
- In addition to you, there is already a team with Data culture who can understand the challenges of this type of research and is willing to move in this direction?
- Your team has a Work routine and Knowledge Management that allow relatively simple individual plans to be carried out in parallel (for example, some TCC), guided by milestones that support more sophisticated research in the future (for example, a doctoral thesis)?
- Have you ever documented a Training cycle minimum to embark new researchers? Are there more cost-effective alternatives to a training cycle that depends on you? For example, is there already a data science training course that is regularly offered and accessible to potential team members?
- In addition to your subordinates, you have people with knowledge of other areas who are able to confirm viability of his ambition?
- That is, with the objective of conducting empirical research in law (applied social science), you have a Network to evolve in partnership with knowledge of technological support (exact sciences)?
- Are you open to accepting and guide your planning From this feasibility analysis, combining immediately viable research projects and a horizon of innovation to be explored?
- The results of the research can be incorporated into products that have value for the market ? You already have a plan to have Market access ?
Of course, this is not a single path. There are several types of laboratories, especially when it comes to the university context, in which a large part of the resources of the laboratories are demands for teaching or basic research activities. But if you're involved in building a laboratory that has a legal purpose and works with data, you may want to take certain precautions. After all, technology is not his main area.
In conclusion, Building a laboratory is not the same as buying equipment . A laboratory is built around problems to be solved. And these are not small problems, as they require collaboration from different areas to be overcome. The work environment and culture of this group of people are the foundations of the laboratory. In fact, it is something quite intangible.
In a world in which technological infrastructure has started to be consumed as a service (cloud computing), having physical resources is no longer an absolute competitive advantage. The real challenge is to develop a work that reconciles research and innovation with the urgency and pragmatism demanded by the market.
After all, in this area, without the market, there is no funded research. And, without money, the other conditions to create and maintain a laboratory of this type will not be present. My recommendation is that you don't go shopping on the first day, because first you need to answer the list of questions listed at the beginning of the post.
PS: While writing the post, I learned that the CNJ, by Ordinance 25/19, created a laboratory (called Inova PJe) and an Artificial Intelligence Center. I don't think the reflections in the post are fully applicable to institutional laboratories. In fact, I see the CNJ more as a decision-making body than an operational one. The operation itself would take place, for example, in an agreement with an academic laboratory, whose operation I described in the post.
I've tried all kinds of organizations to be a productive advisor and thus have a good research group. It's not an easy task, but – as a teacher – it's something you need to face head-on. Otherwise, he will be demanded repeatedly, always providing the same information, with each passing semester, to different advisees.
Tool-based organization is essential in the preparation of orientation meetings. If this is done, the advisor becomes agile in providing the sources so that the advisee can read them before this personal meeting. And the advisee will have a more productive meeting, which is exactly what everyone wants. This way it is possible to advance in the research more quickly.
Project bank
In my opinion, for the initial phase of the research, it is essential to maintain a project bank that can be easily updated and shared with the advisees. While I've tried in the past to automate this part of the workflow with a database (including a query link and publicly available filters), it doesn't seem like a good idea anymore.
Today my option is to personally manage the project bank and only send to each new one guiding a block of 10 to 20 projects related to the theme of their interest. After reading 20 good projects, it is very likely that the advisee will return, for a second meeting, with a project of the same quality.
This is my first conclusion: in order for the student to write a good project, he needs to read several others.
Without reading dozens of projects, the student does not internalize what he needs. He is left without reference, because his own project is practically the only one he will read in his life. The effort in this part really pays off, no matter if you supervise a graduation paper or review a thesis project. So, if you are a teacher, my suggestion is that you keep this bank from the selection of all the projects you read. It takes work and time, but the return is guaranteed.
There are several options to help maintain this bank. You can use Airtable to create a document database and share the link with your mentee. If you need inspiration, check out this template . Although the template is from a book catalog, you just have to adapt it to become a project catalog. If you want any options, see the Zenkit , which is practically a clone of Airtable. That's all you need.
Reference Library
It is nostalgic to think that someone can develop any research just by visiting the library to make copies of paper books. In fact, we all started the search on the internet. This also leads us to a common problem, consisting of the challenge of maintaining an electronic collection of books and articles ready for consultation and citation.
If, on the one hand, it is true that an undergraduate student does not need all this sophistication to finish a final paper, it is very likely that a professional researcher has, whatever it may be, his way of organizing a digital library.
Let's evaluate some scenarios. The person has a folder full of pdf files on their computer. It certainly works, despite the obvious limitations. A popular variation is the shared folder in Dropbox. But these options are just ways to store files. What a professional researcher - and one who is modern - demands is beyond that. After all, you need to manage the citations in an integrated way with your text editor or, at least, generate the bibliography with one click.
I think there are pretty consistent options on the market for this type of task. The most popular (and which has an affordable paid premium plan) is the Mendeley . In the past, I think that the Endnote It was once a serious option, but it's even more expensive. I prefer the alternative to open source, called Zotero . For me it is the definitive solution.
This is my second conclusion: in order for the guidelines to be able to provide references quickly, its reference library needs to be organized.
Conclusion
For the advisor, it is worth investing in the organization of a project base, as this saves work and increases the quality of the project to be prepared by the advisee. Once the project has been prepared, it is worth investing in the organization of a reference library to help improve the text.
Together, these tools prevent each orientation process from becoming the reinvention of the wheel. And we certainly don't need that.
I've tried all kinds of organizations to be a productive advisor and thus have a good research group. It's not an easy task, but – as a teacher – it's something you need to face head-on. Otherwise, he will be demanded repeatedly, always providing the same information, with each passing semester, to different advisees.
Tool-based organization is essential in the preparation of orientation meetings. If this is done, the advisor becomes agile in providing the sources so that the advisee can read them before this personal meeting. And the advisee will have a more productive meeting, which is exactly what everyone wants. This way it is possible to advance in the research more quickly.
Project bank
In my opinion, for the initial phase of the research, it is essential to maintain a project bank that can be easily updated and shared with the advisees. While I've tried in the past to automate this part of the workflow with a database (including a query link and publicly available filters), it doesn't seem like a good idea anymore.
Today my option is to personally manage the project bank and only send to each new one guiding a block of 10 to 20 projects related to the theme of their interest. After reading 20 good projects, it is very likely that the advisee will return, for a second meeting, with a project of the same quality.
This is my first conclusion: in order for the student to write a good project, he needs to read several others.
Without reading dozens of projects, the student does not internalize what he needs. He is left without reference, because his own project is practically the only one he will read in his life. The effort in this part really pays off, no matter if you supervise a graduation paper or review a thesis project. So, if you are a teacher, my suggestion is that you keep this bank from the selection of all the projects you read. It takes work and time, but the return is guaranteed.
There are several options to help maintain this bank. You can use Airtable to create a document database and share the link with your mentee. If you need inspiration, check out this template . Although the template is from a book catalog, you just have to adapt it to become a project catalog. If you want any options, see the Zenkit , which is practically a clone of Airtable. That's all you need.
Reference Library
It is nostalgic to think that someone can develop any research just by visiting the library to make copies of paper books. In fact, we all started the search on the internet. This also leads us to a common problem, consisting of the challenge of maintaining an electronic collection of books and articles ready for consultation and citation.
If, on the one hand, it is true that an undergraduate student does not need all this sophistication to finish a final paper, it is very likely that a professional researcher has, whatever it may be, his way of organizing a digital library.
Let's evaluate some scenarios. The person has a folder full of pdf files on their computer. It certainly works, despite the obvious limitations. A popular variation is the shared folder in Dropbox. But these options are just ways to store files. What a professional researcher - and one who is modern - demands is beyond that. After all, you need to manage the citations in an integrated way with your text editor or, at least, generate the bibliography with one click.
I think there are pretty consistent options on the market for this type of task. The most popular (and which has an affordable paid premium plan) is the Mendeley . In the past, I think that the Endnote It was once a serious option, but it's even more expensive. I prefer the alternative to open source, called Zotero . For me it is the definitive solution.
This is my second conclusion: in order for the guidelines to be able to provide references quickly, its reference library needs to be organized.
Conclusion
For the advisor, it is worth investing in the organization of a project base, as this saves work and increases the quality of the project to be prepared by the advisee. Once the project has been prepared, it is worth investing in the organization of a reference library to help improve the text.
Together, these tools prevent each orientation process from becoming the reinvention of the wheel. And we certainly don't need that.
US President Donald Trump recently signed (11/02/19) a " executive order " to, in these words, "maintain the leadership" of the country in the field of artificial intelligence. Although it is undeniable that the US plays a very important role in this area, it is not so simple to position oneself as a leader. In fact, the very concern with maintaining a supposed lead demonstrates that there is at least one serious threat in this race for AI, in which China has been standing out a lot.
More than a promise: years of budget
To carry out this mission, a Committee linked to the National Science and Technology Council (NSTC) was appointed, in such a way that broad coordination of the American federal government, including all its agencies, is expected. Directors of these agencies are encouraged, from now on, to prioritize investments in AI, making their budget proposals contemplate investments in the area and, especially, during the coming years.
In other words, there is a concern to provide funds for the initiative and the program recognizes that the development of AI is something that, in addition to money, also consumes a lot of time. And this coexists with a sense of urgency, as the act sets a deadline of 90 days for each agency to indicate how it intends to commit its annual budget to achieve the objectives set by the rule.
Strategic principles and objectives
Trump's act is guided by five principles: promotion of science, economic competitiveness and national security; lowering barriers to AI experiments in order to broaden its use; educating citizens to face the economic revolution caused by technology; guarantee of civil liberties and privacy; as well as maintaining the strategic position of the US in the world AI market.
It seems like a good summary of everything this technology promises in terms of advances and also risks arising from it. Thus, at the same time that Trump reinforces the strategic importance of being a protagonist in the export of AI, he delimits that this asset must be protected so that it does not fall into the hands of commercial adversaries and, especially, enemies. Trump is also committed to maintaining the employability of American citizens, in view of the announced extinction [in my view, prematurely] of several professions.
The principles listed should be aimed at, within the scope of the federal government, achieving six strategic objectives: converting AI research into innovation applied to practice; increase the supply of data and expand access to specialized computers; preserve security and privacy, even in the face of the expansion of AI uses; reduce the vulnerability of systems to malicious attacks; ensure that public and private employees are able to use new technologies; and, finally, to maintain US leadership in the area.
The timeline and deadlines
In addition to establishing competencies, principles and strategic objectives, the "executive order" creates a schedule for them to be achieved. The first step is the improvement in the provision of data by the federal government, which is recognized as a bottleneck for the development of AI.
A public call is planned to, within 90 days, identify the demands of civil society and academia in relation to which services should be prioritized. Within 120 days of the publication of the act, with the support of the Ministry of Planning (OMB), the Federal Committee for Artificial Intelligence (Select Committee) must update the guidelines for the implementation of data and software repositories, with the aim of improving the retrieval and use of information.
In addition to these predictions, the "executive order" creates a series of urgent milestones for the cycle to be successful, starting with the demands of data scientists and closing with meeting them. In other words, it is a planning guided by the use and purpose that society wants to give to data. The American government is not saying what should be done, calling itself only the duty to organize the data, so that there are no leaks or violations of privacy.
My Take: Race Against China
In my view, Trump's new act is very correct and reveals the real clash of two powers. China is the leader in information gathering (including questionably) and is rapidly advancing with its processing capacity. The U.S., on the other hand, may even be seen as a leader in AI research, but it depends on more data to keep fighting. Therefore, the act reorganizes the foundations of the American public data structure. After all, without data, it's impossible to make progress in data science.
Apparently, when it comes to AI, judging by the deadlines and milestones outlined in the American standard, time is also money. In fact, a lot of money. Proof of this is that the US is redoing the foundations, and not a mere renovation of the roof.
US President Donald Trump recently signed (11/02/19) a " executive order " to, in these words, "maintain the leadership" of the country in the field of artificial intelligence. Although it is undeniable that the US plays a very important role in this area, it is not so simple to position oneself as a leader. In fact, the very concern with maintaining a supposed lead demonstrates that there is at least one serious threat in this race for AI, in which China has been standing out a lot.
More than a promise: years of budget
To carry out this mission, a Committee linked to the National Science and Technology Council (NSTC) was appointed, in such a way that broad coordination of the American federal government, including all its agencies, is expected. Directors of these agencies are encouraged, from now on, to prioritize investments in AI, making their budget proposals contemplate investments in the area and, especially, during the coming years.
In other words, there is a concern to provide funds for the initiative and the program recognizes that the development of AI is something that, in addition to money, also consumes a lot of time. And this coexists with a sense of urgency, as the act sets a deadline of 90 days for each agency to indicate how it intends to commit its annual budget to achieve the objectives set by the rule.
Strategic principles and objectives
Trump's act is guided by five principles: promotion of science, economic competitiveness and national security; lowering barriers to AI experiments in order to broaden its use; educating citizens to face the economic revolution caused by technology; guarantee of civil liberties and privacy; as well as maintaining the strategic position of the US in the world AI market.
It seems like a good summary of everything this technology promises in terms of advances and also risks arising from it. Thus, at the same time that Trump reinforces the strategic importance of being a protagonist in the export of AI, he delimits that this asset must be protected so that it does not fall into the hands of commercial adversaries and, especially, enemies. Trump is also committed to maintaining the employability of American citizens, in view of the announced extinction [in my view, prematurely] of several professions.
The principles listed should be aimed at, within the scope of the federal government, achieving six strategic objectives: converting AI research into innovation applied to practice; increase the supply of data and expand access to specialized computers; preserve security and privacy, even in the face of the expansion of AI uses; reduce the vulnerability of systems to malicious attacks; ensure that public and private employees are able to use new technologies; and, finally, to maintain US leadership in the area.
The timeline and deadlines
In addition to establishing competencies, principles and strategic objectives, the "executive order" creates a schedule for them to be achieved. The first step is the improvement in the provision of data by the federal government, which is recognized as a bottleneck for the development of AI.
A public call is planned to, within 90 days, identify the demands of civil society and academia in relation to which services should be prioritized. Within 120 days of the publication of the act, with the support of the Ministry of Planning (OMB), the Federal Committee for Artificial Intelligence (Select Committee) must update the guidelines for the implementation of data and software repositories, with the aim of improving the retrieval and use of information.
In addition to these predictions, the "executive order" creates a series of urgent milestones for the cycle to be successful, starting with the demands of data scientists and closing with meeting them. In other words, it is a planning guided by the use and purpose that society wants to give to data. The American government is not saying what should be done, calling itself only the duty to organize the data, so that there are no leaks or violations of privacy.
My Take: Race Against China
In my view, Trump's new act is very correct and reveals the real clash of two powers. China is the leader in information gathering (including questionably) and is rapidly advancing with its processing capacity. The U.S., on the other hand, may even be seen as a leader in AI research, but it depends on more data to keep fighting. Therefore, the act reorganizes the foundations of the American public data structure. After all, without data, it's impossible to make progress in data science.
Apparently, when it comes to AI, judging by the deadlines and milestones outlined in the American standard, time is also money. In fact, a lot of money. Proof of this is that the US is redoing the foundations, and not a mere renovation of the roof.
Working is hard enough. Imagine being accountable... and work. But it is a routine in which we need to invest so that our work is, at least, more seen by colleagues. And this is also important for the improvement of teamwork. In this post I give some tips on how it is possible to organize this routine with some productivity tools.
Using Slack as a remote desktop
The following video shows a simplified routine of accountability. I captured the phone with Jibble on the left and the respective team in Slack. Basically, what the video shows is a user (in this case me 🙂) navigating within the Jibble app on the iPhone and selecting: In/Out → In → Confirm In.
As it is intuitive, for the integration to work, the administrator needs to have created the accounts in both Jibble and Slack. Then the administrator needs to invite the team, by email, to be part of each of the systems. Finally, it will integrate the services, inviting the Jibble bot to inhabit the Slack workspace. But really this is only of interest to the administrator.
From the user's point of view, he will only receive invitations, register his passwords and, if he wishes, will also install the applications on the platforms of his choice. Here we are demonstrating Jibble on the phone and Slack on the computer and the captured routine is the one that will record my check-in.
Since the Jibble team is onboarded through Slack, everyone will be notified in their channel that I'm currently working. Look at the notification that appears in the lower right corner of the screen, at the end of the channel's timeline. When you're done, all I need to do is: In/Out → Out → Confirm Out.
Beyond time control: what was effectively done
This simple interaction will generate enough information to know who worked, for how long, and at what frequency. But the system allows you to know much more about the work. Take, for example, another team I have on Jibble, connected to another workspace on Slack. This other team already has an established culture of accountability in more detail.
There a field called "Optional Note/Task" was enabled, in which the user informs what he intends to do when starting his work shift. Likewise, when checking out, the same field will appear and the user will have the chance to comment on whether or not they have fulfilled what they intended to do.
For experienced users, just inform what you managed to accomplish when checking out, and the first registration is dispensable. On the other hand, experience shows that less diligent users simply may not remember to check out, leaving a gap in the system. That's why it's important to get used to also posting what you intend to do when starting the work shift.
It is not the subject of this post, but Jibble has a number of additional settings that we hope the user will discover when using the application. The first feature that the user will notice is that I always schedule an automatic checkout every four hours. The practical effect is that the "forgotten" user will always be marked by the "self-checkout" and will not have the opportunity to record what he managed to accomplish. Naturally, this is a negative point and one that will be recorded for future evaluation. 😇
PS: Did you like it? Want to know more? See the Help do Jibble about checking in without having to install the app.