Our second example sheds valuable light on the application of nuanced controls. This institution wanted visibility into how, exactly, a machine-learning model was making decisions for a particular customer-facing process. After carefully considering transparency requirements, the institution decided to mitigate risk by limiting the types of machine-learning algorithms it used. Disallowing certain model forms that were overly complex and opaque enabled the institution to strike a balance with which it was comfortable. Some predictive power was lost, which had economic costs. But the transparency of the models that were used gave staff higher confidence in the decisions they made. The simpler models also made it easier to check both the data and the models themselves for biases that might emerge from user behavior or changes in data variables or their rankings.
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