The Risk of Machine-Learning Bias (and How to Prevent It)

As promising as machine-learning technology is, it can also be susceptible to unintended biases that require careful planning to avoid. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Many companies are turning to machine learning to review vast amounts of data, from evaluating credit for loan applications, to scanning legal contracts for errors, to looking through employee communications with customers to identify bad conduct. New tools allow developers to build and deploy machine-learning engines more easily than ever: Amazon Web Services Inc. recently launched a "machine learning in a box" offering called SageMaker, which non-engineers can leverage to build sophisticated machine-learning models, and Microsoft Azure's machine-learning platform, Machine Learning Studio, doesn't require coding. But while machine-learning algorithms enable companies to realize new efficiencies, they are as susceptible as any system to the "garbage in, garbage out" syndrome.