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Artificial Intelligence, Advocacy and Lawtechs – Part 1


Artificial Intelligence (AI) is a field of knowledge that raises many doubts in the legal community and in society, still closely linked to the idea of ​​disseminating robots. It is, therefore, a theme that needs constant deepening, considering that many technologies derived from this area of ​​knowledge are already used in everyday life.

AI is an activity dedicated to making machines intelligent and intelligence is the quality that allows a technology to operate properly and predictably in its environment, which is why there are so many different capacities, which depend on the space in which they work¹. It seeks to find the solution of problems considered complicated by human beings during the course of life, organizing it through the structure of symbols and operations, through which the intellectual solution is carried out together with strategic guidelines to achieve them².

It is associated with the branch of computer science that is concerned with the automation of intelligent behavior, due to theoretical principles in this area, such as the data structures used in the representation of knowledge, the algorithms necessary to apply this knowledge and the languages ​​and programming techniques used in the implementation³.

Among the competencies that are most frequently observed in technological solutions present in law firms and legal departments are machine learning, natural language processing and specialized systems:

(1) Machine learning involves generalization by experience. The performance of the system should improve not only in the repetition of the same task, but also in similar activities in that realistic domain of knowledge. That is, computer programs learn on their own (experience, analogy, examples) using a set of learning principles to provide diversified solutions4;

(2) Natural Language Processing is the attempt to extract a more complete representation of meaning from free text, to discover who did what to whom, when, where, how and why. PLN makes use of linguistic concepts such as the part of speech (noun, verb, adjective, among others) and grammatical structure (nominal or prepositionable phrase, or dependency relations as a subject or object). This technology has to deal with anaphor (previous noun that a pronoun or other later reference phrase corresponds to) and ambiguities (of words and grammatical structures, such as what is being modified by a certain prepositionable word or phrase). To this end, it applies various representations of knowledge, such as the lexicon of words and their meanings, properties and grammatical rules, in addition to a thesaurus, abbreviations and ontology of actions5;

(3) Expert knowledge is a combination of theoretical understanding of the problem and a collection of heuristic problem-solving rules that experience has shown to be effective in its domain. Specialized systems are built by obtaining this knowledge from a human specialist, who provides the necessary knowledge through a general discussion of their problem-solving methods, along with demonstration in a particular sample of situations, passing the coding so that the computer can apply it. it to similar problems6.

The application of AI resources in legal services must therefore result from an in-depth analysis of the functioning of the office or legal department, with a focus on customer service management, covering clear objectives of reducing costs and increasing efficiency. In addition, it is necessary to observe the relevance of these innovations to the reality and particularities of each law firm or legal department.

By: Wilson Sales Belchior


¹ NILSSON, Nils J. The quest for artificial intelligence: a history of ideas and achievements. Cambridge: Cambridge University Press, 2010.

² KORNIENKO, Alla A. et. al. Knowledge in artificial intelligence systems: searching the strategies for application. Procedia – Social and Behavioral Sciences, Atlanta, n. 166, p. 589-594, 2015.

³ LUGER, George F. Artificial intelligence: structures and strategies for complex problem solving. Boston: Pearson, 2009.

4 Idem.

5 KAO, Anne; POTEET, Stephen R. Overview. In: KAO, Anne; POTEET, Stephen R. (Eds.). Natural Language Processing and Text Mining. London: Springer, 2007. p. 1-7.

6 LUGER, George F. Artificial intelligence, op. cit.


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