Human-Centric AI at Thomson Reuters
Trust is at the core of everything we do at Thomson Reuters, including the design, development, and deployment of AI systems. In the spirit of our Trust Principles we need to ensure that we are also the provider of trustworthy AI solutions. AI and its use are currently largely unregulated and globally adopted standards and principles are in development. At Thomson Reuters we are contributing to the progress of these domains, by ensuring we not only strengthen trust in our own AI systems, but that we are supporting the progress of trustworthy AI throughout society.
The Human-Centric AI research theme is a multidisciplinary approach to addressing the challenges AI faces in terms of full adoption and trust. It gains in importance as human workflows are increasingly intertwined with AI systems to support them. It is closely tied to AI Ethics concepts such as interpretability, explainability, transparency, bias/fairness, privacy, security and societal impact, which are central concepts in TR’s AI Principles. We are investigating how to design, build, test and deploy AI systems with a human-centric mindset, and are establishing thought-leadership in this domain in collaboration with internal stakeholders, industry partners, and universities.
The objective of our research in this domain is to demonstrate how we put our AI Principles into practice; maximize the effectiveness of AI features we build and deploy; and to ensure we are making our best efforts to address complex questions under this theme.
Norkute, Milda, Nadja Herger, Leszek Michalak, Andrew Mulder, and Sally Gao. 2021. “Towards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization.” In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. CHI EA ’21. New York, NY, USA: Association for Computing Machinery.
Schleith, Johannes, Nina Hristozova, Brian Chechmanek, Carolyn Bussey, and Leszek Michalak. 2021. “Noise over Fear of Missing Out.” In Mensch Und Computer 2021 - Workshopband, edited by Carolin Wienrich, Philipp Wintersberger, and Benjamin Weyers. Bonn: Gesellschaft für Informatik e.V.
Related research areas
Natural Language Processing
Natural Language Processing (NLP) focuses on designing algorithms to parse, analyze, mine, and ultimately understand and generate human language. NLP with a focus on text data, is one of our core enabling technologies given our customers’ work in information heavy segments.
AI DevOps (ModelOps)
We are exploring methods and technologies related to the emerging domain of ModelOps. This field combines AI development and IT operations with the objective to shorten the "AI Lifecycle", provide continuous delivery, and increase the quality of what we deliver to our customers.
Information Retrieval and QA
Our customers need the right information, in the right context, and often under tight time constraints. We adopt a comprehensive approach to the information findability problem, using a combination of search technologies, recommendation systems, and navigation-based discovery.