For successful machine learning tools, talk with end users

Machine learning tools are used in a variety of fields, from sales to medicine. But getting tech into the workplace is just one step — these tools are only successful if they’re integrated into workflows, and if people trust them enough to depend on them.

A key to successful adoption is back-and-forth dialogue between technology developers and end users, according to new research from MIT Sloan professorKate Kellogg,Sara Singer of Stanford University, Ari Galper of Columbia University, and Deborah Viola of Westchester Medical Center. The paper was published in Health Care Management Review.

Deploying workplace tools is often seen as one-directional — developers make them and hand them off to users. This doesn’t work for machine learning tools, the researchers write, because of the data required to train machine learning models and the opacity of the models. Both developers and end users might not understand how time-intensive this process is, and how much patience and input is required.

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