Accurate and reliable AI: Four key ingredients

The widespread adoption of generative artificial intelligence (AI) tools across various industries has garnered significant interest and sparked discussions among professionals.

The second annual Future of Professionals Report from the Thomson Reuters Institute reinforces this perspective, showing that respondents see AI as a pivotal force for the future of professional practices and methodologies. A striking 77% of respondents anticipate that AI will have a significant or transformative influence on their professional activities within the next five years.

With the fast-paced changes seen in today’s professional environment, how do you understand and evaluate the accuracy and reliability of this transformative technology?

Here are four critical ingredients that go into making reliable and accurate AI.

High-quality data

You must train AI technology on high-quality, authoritative data free from biases and representative of real-world scenarios in which professionals operate. Authoritative data in AI encompasses highly reliable and accurate sources recognized for their credibility, which is crucial for training AI models to ensure they operate on validated information. This data provides a robust foundation for AI systems to analyze patterns, make decisions, and learn effectively.

Domain and technical expertise

Involving domain and technical experts in the testing and validation of AI systems is crucial to ensure that AI meets end users' specific needs. Both experts play a pivotal role by generating and driving the training data and substantiating the performance of machine-learning algorithms. Their deep understanding of the domain and data attributes allows them to identify and explain the sources of errors, facilitating targeted improvements in algorithm tuning and development.

Security

For AI to be truly reliable across various industries, it must be developed and deployed with stringent security measures such as encryption, authentication, and regular auditing. Implementing robust security measures protects highly confidential data from unauthorized access and manipulation. A secure AI environment not only boosts confidence in the technology but also enhances its effectiveness, ensuring consistent performance and compliance with regulatory standards across applications.

Ethics

Ethical considerations, particularly regarding data privacy and the avoidance of bias, must be rigorously addressed to build confidence among professionals and clients. Ethics guide the creation of algorithms that fairly and responsibly handle data, preventing biases and ensuring inclusivity. By adhering to principles of transparency and accountability, ethical AI fosters trust among users. When they prioritize ethical considerations, AI developers can create systems that perform efficiently, respect user privacy, and promote fairness, making them more dependable and acceptable to a broader audience.

Professional-grade GenAI you can trust

Accuracy and reliability are the foundation of delivering professional-grade AI and technology. CoCounsel, the GenAI assistant from Thomson Reuters, is the result of our commitment to trusted innovation. CoCounsel is rooted in market-leading, verified content databases and understands professional standards, allowing it to deliver reliable results with great speed. Most importantly, it features end-to-end encryption, ensuring your data remains private and secure.

When innovation opens the door to exciting possibilities, professionals can trust us to provide trusted solutions backed by decades of subject matter and technological expertise.