Legal Logic Design
Tom holds a JD from Stanford with a background in legal operations and management consulting. Prior to law school, Tom was a partner in legal operations at Google, where he led cross-functional projects focused on legal technology, knowledge management and process improvement. Prior to Google, Tom worked in the federal practice of Deloitte Consulting with a focus on health technology. Tom currently supports the Corporate Legal Operations Consortium (CLOC), a non-profit group dedicated to setting standards and best practices in legal services of all sizes around the world. At Stanford, Tom is co-director of the International Refugee Assistance Project and OutLaw, in addition to his participation in the Legal Design Lab. Tom graduated in 2012 from American University with a bachelor`s degree in political science and law and society. Modelling legal decisions means that the use of law to decide certain cases can follow a fixed pattern of legal decisions. The modelling of court decisions requires three conditions. The first condition is that the judge must be a rational person, he must maintain a sober understanding, be able to identify the advantages and disadvantages, find the key points and without personal calculation of gains and losses.
At the same time, it also has the ability to predict the maximum expected value. The second condition is that some cases can be typed. Legal arbitrators are able to categorize a variety of cases, eliminate too many details from cases, and preserve the body of cases. Adjudicators should know not only how to record the facts to be proven in these cases, but also the applicable law of these cases. The third condition is that the arbitrator can make “the same legal decision for the same cases”, he can also make “the different legal decision for different cases”. The third condition implies the premise that a distinction is made between “the same cases” and “different cases” and that the arbitrator, as a rational person, can distinguish between them. Shahid Rahman is an exceptional class in logic and epistemology at the University of Lille-Nord-pas-de-Calais, Human and Social Sciences. He is also a researcher at UMR-CNRS 8163:STL.
She has been working with the Legal Design Lab for the past three years on projects such as creating the Navocado platform to train pro bono lawyers in new areas, improving the accessibility of online legal aid, and optimizing Internet search engines for legal queries. Unfortunately, many lawyers and judges lag behind when it comes to effectively presenting statistical information in legal writing. For example, in Board of County Commissioners of Park County v. Water Quality Control Commission of the State of Colorado, the Court found that the Board`s water quality standards authorizing high levels of cadmium and lead were based on insufficient statistical evidence.  The common practice for summarizing data variations is to graph them.  However, no diagram has been provided in this decision to illustrate the differences between this evidence. In contrast, the statistics chapter of the Federal Reference Manual for Scientific Evidence often uses graphs to describe the statistical data and the conclusions that can be drawn from them.  Legal logic as a research tool for the unification of legal theory and practice, it presents the advantages of parallel development and mutual integration with “artificial intelligence and law.” Legal logic, however, is not a purely technical study of deductive reason; Attention must also be paid to social realities and other important values of the law, such as moral requirements, political orientation, etc. Legal logic is not a mechanical legal application or a unilateral legal method that requires dimensional reflection and consideration. The application of legal logic must meet the requirements of judicial practice, such as the rationality of legal rules, the reliability of facts, acceptance of court decisions, etc. In short, the question of legal logic is man, not artificial intelligence.
Llewellyn, op. cit. Cit. note 32, p. 77 (in describing “logical scales,” Llewellyn explains that it is for the judge to decide what “leads to a right conclusion or a wise conclusion – if he sees two clear possibilities”). Hans Christian Nordtveit Kvernenes is currently a PhD student in philosophy at Savoirs, Textes et Langage, University of Lille 3. His project combines logic with analogous thinking in European law, “A Dialogical Framework for Analogy in Legal Reasoning – The Ratio Legis and Precedent Case Models”. State Farm Gen. Insur., PA-2015-00004, (8.
November 2016) (decision of the Administrative Court of precedents), available at www.insurance.ca.gov/0250-insurers/0500-legal-info/0600-decision-ruling/0100-precedential/statefarmgeneralpa201500004.cfm (last visited on 17 November 2020). Once the applicable rules and principles are derived, legal reasoning becomes deductive in an informal sense, based on the likelihood that a conclusion will be best.  Analogies can also be used to reach a conclusion. In other words, legal reasoning may involve an interaction of analogy, generalization, and informal inference to reach a conclusion.  This interaction allows judges and lawyers to deal with complex issues through long chains of proposals.  And in this way, formal logic can reinforce legal conclusions or reveal errors in them. Causality is the science of “why”, studying causality helps people better understand their own cognitive processes, master causal relationships and build causal thinking, artificial intelligence can make it more powerful. First, build a causal association based on the data association.
In the context of the big data era, the most common form of causal association is data association. The digitization of object objects lays the foundation for the analysis of their association, and the most direct goal of statistical science is to discover the objective laws behind the data. Statistical evidence is increasingly used as forensic scientific evidence, data technology has become an indispensable tool for experts; They gather relevant information by analyzing the data. Second, establish a strong conditional relationship based on empirical observations to ensure that the sufficiency of conditions has a sufficient empirical basis. The fixed causal relationship does not reside in the sequence of isolated events, but in a conditional network of relationships. The sufficient condition between cause and effect not only has a logical relationship, but is also supported by human experience. In short, strengthening research on the logical intelligence of causality will help achieve the transition from weak AI to strong AI to further promote the intelligent expansion of legal logic. Whether in a majority or dissenting opinion, the legal conclusions could have been less mysterious and better explained by printing the applicable law in its entirety to visualize the metaphorical gaps that are filled or not filled. The difference between the majority and Gorsuch`s dissent is that the majority viewed the language of worker protection in the broader context of the subject matter, unlike Gorsuch J.
The mere inclusion of the relevant language in such boxes allows the reader to determine for himself whether the dissent would have led to absurd results. Laws can also conflict. A complex set of laws can be applied to a dispute. Or, the law may not have been drafted to resolve disputes about unforeseen circumstances or those in which Parliament was unable or unwilling to act. In fact, Parliament may leave certain ambiguities in the law to give judges the discretion to apply it to unforeseen circumstances. In such cases, lawyers try to persuade judges to accept their views on the purpose of the applicable law and the facts in a narrative that convinces the judges of a most favourable outcome for their client.  Llewellyn describes this as a one-off approach to logic, looking not for a rule or main premise that applies “widely,” but a premise that applies to the “question under consideration.”  The publication examines new perspectives on the interaction between logic and law, specifically the study of different responses to the question: What role does logic play in legal thought? Different perspectives range from basic studies (such as logical principles and frameworks) to historical applications and perspectives. In an attempt to describe the logical relationship between two concepts without formal logic, judges began to use or refer to Venn diagrams.  This section provides examples of Venn diagrams in court decisions aimed at introducing this important form of informal logic. This article focuses on how the logic of legal reasoning can be better communicated using visuals. Until now, visuals have focused on the logical relationships of containment, overlap and exclusion that are quantitative. This section focuses on visualizing generalizations, which are conclusions drawn from evidence or data that can be quantified.
Recall in Section II.C that a generalization is only a conclusion drawn from a variety of specific information. Since the collection, analysis and interpretation of data are subject to statistics, such logical conclusions are called statistical conclusions. In 1897, Oliver Wendell Holmes Jr. wrote that “for the rational study of law, the black letter may be the man of the present, but the man of the future is the man of statistics and the master of economics.”  Some argue that the future Holmes had foreseen is yet to come.  But an examination of the thousands of appeal cases with statistical evidence shows that the future Holmes envisioned has already arrived.