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AI @ Thomson Reuters

Events: See the Labs Work in Action! 

Our team regularly participates in industry conferences, seminars, and workshops. We also host events with AI ecosystem leaders and customer audiences. Check out our upcoming activity below.  


September 24, 2021

AI Summit and Career Fair 2021 

Date: September 24, 2021 
Location: Virtual
Event Details: View Here

Abstract

Vector’s annual AI Summit & Career Fair (held virtually) provides an opportunity for AI graduate students, researchers & alumni* to hear from industry leaders, practitioners, and researchers in AI. Explore career opportunities at organizations leading AI research and adoption, learn about what it takes to get hired as a recent graduate, and network with students and alumni across the province. 


October 5-6, 2021

Legal Geek Conference 

Date: October 5-6, 2021  
Location: London, England
Event Details: View Here

Abstract

The Legal Geek Conference is back! We are bringing together law firm leaders, general counsel, investors, scaleups, policymakers, startups and innovation-fanatics for two days of inspiration and knowledge-sharing. We’re bringing the community back together. Check out the Labs in the speaker sessions, exhibition area and workshops. 


October 14-15, 2021

Data Innovation Summit 2021 

Date: October 14-15, 2021   
Location: Kistamässan, Stockholm
Event Details: View Here

Abstract

Data Innovation Summit is constructed so it equally addresses all the elements of data-driven and AI-ready business: data, people, processes and technology. The event is built to be both business and technical, practical and inspirational, realistic and futuristic, educational and exciting, regional and global, live and digital, general and niched, inspiring and influential.


December 7-10, 2021

Mining and Learning in the Legal Domain 

(MLLD2021)

Date: December 7-10, 2021
Location: Auckland, New Zealand
Event Details: View Here

Abstract

In conjunction with the 21st IEEE International Conference on Data Mining, December 7-10, 2021, Auckland, New Zealand

The increasing accessibility of large legal corpora and databases create opportunities to develop data driven techniques as well as more advanced tools that can facilitate multiple tasks of researchers and practitioners in the legal domain. While recent advancements in the areas of data mining and machine learning have gained many applications in domains such as biomedical, healthcare and finance, there is still a noticeable gap in how much the state-of-the-art techniques are being incorporated in the legal domain. Achieving this goal entails building a multi-disciplinary community that can benefit from the competencies of both law and computer science experts. The goal of this workshop is to bring the researchers and practitioners of both disciplines together and provide an opportunity to share the latest novel research findings and innovative approaches in employing data analytics and machine learning in the legal domain.


February 22-March 1, 2022

AAAI conference series 

Date: February 22-March 1, 2022
Location: Vancouver, BC, Canada
Event Details: View Here

Abstract

The purpose of the AAAI conference series is to promote research in artificial intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers in AI and its affiliated disciplines. AAAI-22 is the Thirty-Sixth AAAI Conference on Artificial Intelligence. Like previous AAAI conferences it will feature technical paper presentations, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, all selected according to the highest standards. AAAI-22 will also include additional programs for students and young researchers.

Previous events


June 17, 2021

AI Ethics: It’s Time 

Date: June 17, 2021
Location: Virtual
Event Details: View Here

Abstract

As Artificial Intelligence (AI) and autonomous decision-making systems take hold and rapidly develop, how can Canada ensure this powerful technology upholds our most important values? Fairness, inclusion, democracy, individual privacy, economic security, public safety and sustainability.  Ethical AI: It's time  — brought to you by Thomson Reuters; Communitech, the champions of Tech for Good®; and CityAge — brings together some of the top minds in AI, business and society to drive conversation around how we can ensure this transformative and rapidly evolving technology benefits everyone. We have a duty in front of us to leverage these technologies to solve some of the world’s most pressing challenges, but we must do so safely. 


May 31-June 04, 2021

Symposium on AI and Law  

Date: May 31-June 04, 2021
Location: Virtual
Event Details: View Here

Abstract

The Symposium on Artificial Intelligence and Law (SAIL) is a five-day, virtual event that aims to bring together experts from Industry and Academia to discuss the future of AI and Law. The Symposium aims to provide a venue for academic and industrial/governmental AI-Law researchers and law professionals to come together, present and discuss research results, use cases, innovative ideas, challenges, and opportunities that arise from applications of AI in the Legal Domain. The symposium is also meant to foster collaborations between the Legal and the Artificial Intelligence, Data Mining, Information Retrieval, Natural Language Processing, and Machine Learning communities.


May 11, 2021

CHI 2021: Towards Explainable AI

Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization

Date: May 11, 2021
Location: Virtual
Event Details: View Here

Abstract

This study tested two different approaches for adding an explainability feature to the implementation of a legal text summarization solution based on a Deep Learning (DL) model. Both approaches aimed to show the reviewers where the summary originated from by highlighting portions of the source text document. The participants had to review summaries generated by the DL model with two different types of text highlights and with no highlights at all. The study found that participants were significantly faster in completing the task with highlights based on attention scores from the DL model, but not with highlights based on a source attribution method, a model-agnostic formula that compares the source text and summary to identify overlapping language. The participants also reported increased trust in the DL model and expressed a preference for the attention highlights over the other type of highlights. This is because the attention highlights had more use cases, for example, the participants were able to use them to enrich the machine-generated summary. The findings of this study provide insights into the benefits and the challenges of selecting suitable mechanisms to provide explainability for DL models in the summarization task.