Leveraging Machine and Human Learning with Human-in-the-Loop Models
Our approach empowers people to work alongside machines, enabling us to create more transparent and explainable AI models that are more beneficial to both individuals and businesses.
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.
Nebuli’s Human-centric Augmented Intelligence approach prioritises the involvement of human intelligence in the development process of data models, as it leads to more effective, ethical, and beneficial machine learning algorithms for teams and users.
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.