Communications of the ACM
Toward Practices for Human-Centered Machine Learning
Credit: Andrij Borys Associates, Shutterstock
Machine learning (ML) has been described as a modern Oracle of Delphi—a way to quickly solve problems in different domains, whether auto-completing email messages or predicting the presence of malignant tumors. Computer scientists are theorizing and designing ML technology into our social and personal lives. ML has justifiably stirred tremendous excitement in research, industry, and the popular zeitgeist of artificial intelligence (AI).
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However, the enthusiastic adoption of ML has also had negative consequences. ML is being used for unsavory and controversial purposes, such as generating "deep fake" videos and reproducing facial discrimination.8,41 On the research side, new research points to worrisome trends of chasing metrics over more principled approaches and questionable gains in deep learning's performance compared to linear models.10,17,30 As a researcher working in applied machine learning for mental health, I have seen the consequences of this firsthand, where principled approaches to methods are neglected13 and people become the objects of prediction in a pipeline.11 Computer science (CS) is at a stage where interest in ML has both brought energy to new questions as well as raised reasonable concerns.
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