Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machine-learning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, will still face the challenge of heterogeneity and must approach their task of regulating machine learning with agility. They must build up their capacity in data sciences, deploy flexible strategies such as management-based regulation, and remain constantly vigilant. Regulators should also consider how they can use machine-learning tools themselves to enhance their ability to protect the public from the adverse effects of machine learning. Effective regulatory governance of machine learning should be possible, but it will depend on the constant pursuit of regulatory excellence. 

By Cary Coglianese[1]


Machine-learning algorithms increasingly drive technological advances that deliver valuable improvements for society and the economy. But these algorithms also raise important concerns. The way machin


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