Artificial intelligence at Thomson Reuters

In vertical industries, AI-powered applications often require three key ingredients: data, subject matter expertise, and technology.

  • Data or Content is key because it is often the input and the output of the process — it is the oil that makes everything run. Data is necessary to train machine-learning algorithms and — in our case — it’s often the reason our customers come to us for in the first place. By data, we mean large quantities of content that is current, accurate, comprehensive, and enhanced. At Thomson Reuters, data takes on even a more central role because we operate in data-driven industries, such as the law. Our common law system is, by definition, data-driven — it is a collection of statutes, regulations, case law and other legal and administrative opinions that collectively represent the data that attorneys and judges must research, analyze, interpret, and reason over. This is why, for more than 130 years, we have been collecting, organizing, and enhancing this legal data for our customers. For most of our history, we did this manually; but for the last quarter-century, we developed dozens of machine-learning capabilities to automated many of these tasks so our attorney editors and tax professionals can focus on those matters that require their keen insights and expertise.
  • Subject Matter Expertise is crucial because it ensures we are solving the right problems, that we are able to capture the nuances of the domain in a way that we can understand and analyze. Subject matter experts drive the generation of training data and validate the performance of machine-learning algorithms. Equally important, subject matter experts play a critical role in error analysis; they help scientists and engineers understand the domain and data attributes that are responsible for errors, which drives subsequent iterations of algorithm-tuning and development.
  • Technology drives solution design and development, including that in AI and machine learning. For most of our applications, developing a solution is much more than simply applying an algorithm to a problem or a data set. It often requires designing robust and complex solution architectures that are able to deliver the target functionality at scale. This is why successful AI scientists combine significant expertise in AI tools and technologies, with significant analytical and problem-solving skills as well as a solid understanding of the target domains.


At Thomson Reuters, we operate at the intersection of these three ingredients. Our data is second to none; our subject matter experts have thousands of years of collective experience in their respective domains; and our scientists and engineers are highly skilled in their art and have been developing AI-powered applications for more than 25 years.

Chapter One

Our AI principles

At Thomson Reuters, trust is one of our most important values. Thomson Reuters has drafted the following AI principles to promote trustworthiness in our continuous design, development, and deployment of AI. 

These AI principles will evolve as the field of AI and its applications matures: 

  1. That Thomson Reuters will prioritize safety, security, and privacy throughout the design, development and deployment of our AI products and services.
  2. That Thomson Reuters will strive to maintain a human-centric approach and will strive to design, develop and deploy AI products and services that treat people fairly.
  3. That Thomson Reuters aims to design, develop and deploy AI products and services that are reliable and that help empower people to make efficient, informed, and socially beneficial decisions.
  4. That Thomson Reuters will maintain appropriate accountability measures for our AI products and services. 
  5. That Thomson Reuters will implement practices intended to make the use of AI in our products and services explainable.