Leveraging Machine and Human Learning with Human-in-the-Loop Models
What is Human-in-the-Loop?
Human-in-the-loop (HITL) is a collaborative approach to AI development, where humans work together with machine learning algorithms. Human-in-the-loop involves a feedback loop between the algorithms and the users, where each user can provide feedback to the algorithm, and the algorithm provides suggestions to the user. This approach creates a partnership between humans and machines that leverages the strengths of both, and it is the critical element of our Human-centric Augmented Intelligence models.
Improving Customer Trust
Human-in-the-loop models are a critical investment toward increasing customer trust and loyalty for businesses and teams. By prioritising transparency and explainability, these models enhance customer confidence in AI-based deployments, data practices and digital transformation strategies.
Furthermore, human-in-the-loop offers effective decision-making, providing context and understanding that machines and AI systems lack. It is about significantly improving the effectiveness, efficiency, and ethicality of AI-based solutions and innovations.
Human-in-the-loop models are closely related to the concept of explainable AI – the ability of AI systems to explain how they arrived at a particular decision, generated output, or recommendation. By involving people in the development process, our human-in-the-loop models are explainable and transparent by design.
Our objective is to achieve greater trust and confidence in AI solutions for customers and communities.