AI & Patent Data Workshop

Patents are legal documents in many ways comparable to contracts, wills, and other legal instruments. Patents are typically written by attorneys who employ drafting strategies designed to effectuate various legal objectives. Patent claims are analogous to statutes; claims define the legal rights that the patent confers on its owner, and claim meaning is interpreted through adherence to judicially-created canons of construction. Patent claims provide precisely delineated rights that may be enforced against any infringer in the patent’s jurisdiction, whether or not infringement is intentional. Despite these similarities, patents are different from other legal documents, most notably because they mix technical description with legal drafting. Patents therefore require AI techniques customized to this domain.

There is rapidly-growing community of researchers who apply AI techniques to the patent field. The emergence of readily available AI tools has democratized much of the analysis that a decade ago would have required in-depth knowledge of computer programming or econometrics.

More information and Call for Papers is available on the Workshop webpage.


AICOL2020: AI Approaches to the Complexity of Legal Systems

AICOL addresses the ways in which the current information revolution affects basic pillars of today’s legal and political systems, in such fields as e-democracy, e-government, e-justice, transnational governance, Data Protection, and Security.

We are, indeed, dealing with changes and developments that occur at a rapid pace, as the law transforms itself, in order to respond to and progress alongside with the advances of
technology. In addition to the traditional hard and soft law-tools of governance, such as national rules, international treaties, codes of conduct, guidelines, or the standardization of best practices, the new scenarios of the information revolution have increasingly suggested the aim to govern current ICTs-driven societies through the mechanisms of design, codes and architectures. AI approaches to the complexity of legal systems should take into account how the regulatory tools of technology impact on canonical interpretations of the law.

More information and Call for Papers is available on the AICOL webpage.


ASAIL 2020: 4th Workshop on Automated Semantic Analysis of Information in Legal Texts

This workshop will bring together an interdisciplinary group of scholars, academic and corporate researchers, legal practitioners, and legal service providers for an extended, collaborative discussion about applying natural language processing and machine learning to the semantic analysis of legal texts. Semantic analysis is the process of relating syntactic elements and structures, drawn from the levels of phrases, clauses, sentences, paragraphs, and whole documents, to their language-independent meanings in a given domain, including meanings specific to legal information. The range of focal texts includes:

  • statutes, regulations, and court-made pronouncements of legal rules embodying legal norms,
  • textual arguments in legal case opinions interpreting legal norms and applying them in concrete fact situations,
  • legislative and policy-based debates concerning proposed legal norms, their purpose and meaning,
  • actual and proposed contracts that need to be analyzed for the permissions and obligations they encode and their consistency with organizational preferences or legal frameworks.

Researchers have long been developing tools to aggregate, synthesize, structure, summarize, and reason about legal norms and arguments in texts. Current dramatic advances in natural language processing, text and argument mining, information extraction, and automated question answering are changing how automatic semantic analysis of legal rules and arguments will be performed in the future. In particular, the recent breakthrough in natural language processing brought about by neural network models, including transfer learning using complex language models, has created immense new potential for leveraging legal text for technology supporting legal practice, research, argumentation, and decision making. At the same time, increasing awareness of the mandate of ethical use of AI is fueling a debate about the requirements of such systems and motivates important exploratory work on explainable legal AI.

More information and Call for Papers is available on the ASAIL webpage.


XAILA: The EXplainable & Responsible AI in Law Workshop

In the last several years we have observed a growing interest in advanced AI systems achieving impressive task performance. However, there has also been an increased awareness of their complexity and challenging consequences of their possibly limited understandability to humans. In response, a number of research directions have been initiated. These include humanized or human-centered AI, as well as ethically aligned, ethically designed, or just ethical AI. In many of these ideas, the principal concept seems to be the explanatory capability of the AI system (XAI), e.g. via interpretable and explainable machine learning, inclusion of human background knowledge and adequate declarative knowledge, that could provide foundations not only for transparency and understandability, but also for a possible value alignment and human centricity, as the explanation is to be provided to humans. Recently, the term responsible AI (RAI) has been coined as a step beyond XAI. Discussion of RAI has been again strongly influenced by the “ethical” perspective. However, as practitioners in our fields we are convinced, that the advancements of AI are way too fast, and the ethical perspective much too vague to offer conclusive and constructive results. 

Our objective is to bring people from AI interested in XAI and RAI topics and create an ample space for discussion with people from the field of legal scholarship and/or legal practice, and most importantly the vibrant AI&Law community.

More information and Call forPapers is available on the XAILA webpage.