ʯ����Ƶ��ɫ��

High-quality data

High-quality data is key because it is often the input and output of the process – it is the oil that makes everything run.  must be trained on high-quality, authoritative data free from biases and representative of real-world scenarios in which ʯ����Ƶ��ɫ�� operate — it is often the reason our customers come to us 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 an even 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 ʯ����Ƶ��ɫ�� can focus on those matters that require their keen insights and expertise.

Domain and technical expertise

Human expertise is crucial because it ensures we are solving the right problems, that we can capture the nuances of professional work in a way that we can understand and analyze. Our team of experts is the foundation of our AI. By combining industry expertise with technical know-how, we create solutions that meet the unique needs of our customers.

At Thomson Reuters, our domain experts bring a deep understanding of our customers' daily ʯ����Ƶ��ɫ��s and workflows, which informs the development of our solutions. Meanwhile, our technical experts leverage their skills to build and train AI-powered systems, including machine learning, search, and question-and-answer capabilities, as well as deep learning modules that tackle complex problems and enhance decision-making.

What sets us apart is that many of our technical experts also have domain expertise, allowing us to bridge the gap between technology and industry knowledge. This unique blend of expertise, combined with our commitment to research and development, enables us to deliver solutions that provide accurate, insightful, and actionable knowledge to our customers.

Security

At Thomson Reuters, we prioritize strong security measures in our AI through data impact assessments. This evaluates data governance, privacy, and information security risks for each use case, identifying potential risks and developing mitigation plans. To protect our customers’ sensitive data, we ensure data anonymization, access controls, and security measures like encryption and authentication.

By implementing these robust security measures, we boost confidence in our technology, enhance its effectiveness, and ensure compliance with regulatory standards, ultimately providing a reliable and secure AI environment across various industries.

Ethics

To establish trust with our customers, our AI development prioritizes ethical considerations, such as data privacy and bias avoidance. By designing algorithms at Thomson Reuters that handle data fairly and responsibly, our experts can create inclusive and unbiased AI solutions.

Transparency and accountability are two key principles that guide us. When ethics are at the forefront, AI systems can efficiently process data while respecting user privacy and promoting fairness, ultimately leading to wider acceptance and dependability.