ILTACON Insights: AI-Driven Innovation in Legal Tech
A standout on Day Three of ILTACON was the master class session Artificial Intelligence: The Next Horizon Is Upon Us, Now What? with Johannes Schleith, lead designer, Thomson Reuters, and Borna Jafarpour, senior research scientist, Thomson Reuters.
The session explored AI-driven innovation in legal tech, including the key principles and ideas behind it. Legal Current had the opportunity to talk with Schleith after the session, and below is a recap of the conversation.
Legal Current: As part of Thomson Reuters Labs, what AI trends are you seeing in the legal space?
Schleith: At Thomson Reuters and Thomson Reuters Labs, we have been working on AI-driven innovation for legal technology for decades. AI and machine-learning techniques can be applied to support and assist a range of activities in legal – from classification of legal language, extraction of relevant topics and trends in legal documents, to document summarization and content generation. In the contract analysis space, new technology will go beyond pure clause classification and fact extraction towards assistance of bulk review, document comparison and deviation detection. Another fascinating topic is the AI assistance for knowledge management, more advanced search and question answering across various sources such as legislation, inhouse data rooms, case precedent and guidance notes to assist legal research.
LC: One of the legal use cases for AI involves contract analysis. Can you explain contract analysis and how HighQ Contract Analysis uses AI to help legal professionals with this process?
Schleith: HighQ Contract Analysis uses machine learning and pre-trained models to help legal professionals increase efficiency, reduce risk, and accelerate the contract-review process for transaction due diligence, compliance review and contract investigation. It enables legal professionals to extract facts, definitions and legal clauses in order to answer specific questions they might have for a review – and in a second step, compare such information across documents.
LC: Another AI use case in the legal space involves the shift to remote working and virtual courts. Talk about how AI is valuable for a remote workforce.
Schleith: We have seen attempts to analyze legal claims and anticipate potential outcomes of a case. While this can be interesting for early settlement on smaller issues, the ability for a legal professional to scan through large numbers of documents quickly can also be of interest for larger cases. It is important to consider issues of fairness, bias and explainability of AI systems in this context. AI-enhanced services that assist computer-supported collaborative work are certainly of interest in a time of remote work as well, such as conversational interfaces, automated scheduling or time capture.
LC: What’s the one thing you want your master class participants to understand about AI in legal tech?
Schleith: AI innovation in legal tech is not purely a mathematical question. It requires an interdisciplinary approach involving input from data science, engineering, cognitive science and user experience, as well as subject matter experts and end-users. Successful innovation starts with a detailed understanding of existing processes, pain points and – most importantly – availability and accessibility of data. Best practice involves research and experimentation on various alternative solutions to a problem, supported by continuous feedback loops from end-users and domain experts, in order to inform the design of AI systems.