This is a fantastic piece of work (and paper title!) about the benefits of human-in-the-loop AI processes.
Based on identified user needs, we designed and implemented SMILY (Figure 2), a deep-learning based CBIR [content-based image retrieval] system that includes a set of refinement mechanisms to guide the search process. Similar to existing medical CBIR systems, SMILY enables pathologists to query the system with an image, and then view the most similar images from past cases along with their prior diagnoses. The pathologist can then compare and contrast those images to the query image, before making a decision.
Human-centered tool for coping with Imperfect Algorithms During Medical Decision-Making (via The Gradient)
The system used three primary refinement tools: (1) refine by region; (2) refine by example; and (3) refine by concept. The authors reported that users found the software to offer greater mental support, and that users were naturally focused on explaining surprising results: “They make me wonder, ‘Oh am I making an error?'” Critically, this allowed users some insight into how the algorithm worked without an explicit explanation.
I suspect human-in-the-loop AI processes are our best version of the future. They have also been proposed to resolve ethical concerns.