Human-In-The-Loop AI And Institutionalizing Service Reviews: Building A Culture Of Continuous Operational Learning
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Abstract
The current paper researches the idea of human-in-the-loop (HITL) AI application in supporting an organization to create continuous learning by means of structured service reviews. It describes how AI applications may make summaries, find trends and assist humans’ reviewers in making decisions more. Its study is based on the qualitative approach and is centered on actual cases in the educational, industrial, and security systems. It concludes that the collaboration of people and AI helps to make the process of service review more rapid, regular, and oriented to enhancement instead of criticism. It is also revealed in the study that human feedback aids AI tools in improving in the long run. Such themes as trust, collaboration, and shared learning are brought out. The article gives an example of a model in which AI will help in gathering and analyzing data, and then humans will give insights and context. The authors' results indicate that HITL systems enhance transparency and accountability as well as the decision quality at large. The research concludes that AI-assisted service reviews have a great culture of lifelong learning and enhancement.
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128 Human-In-The-Loop AI And … Sindhu Gopakumar Nair
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