Clearing Opacity Through Machine Learning

Artificial intelligence and machine learning represent powerful tools in many fields, ranging from criminal justice to human biology to climate change. Part of the power of these tools arises from their ability to make predictions and glean useful information about complex real-world systems without the need to understand the workings of those systems.But these machine-learning tools are often as opaque as the underlying systems, whether because they are complex, nonintuitive, deliberately kept secret, or a synergistic combination of those three factors. A burgeoning literature addresses challenges arising from the opacity of machine-learning systems. This literature has largely focused on the benefits and difficulties of providing information to lay individuals, such as citizens impacted by algorithm-driven government decisions.In this Essay, we explore the potential of machine learning to clear opacity that is, to help drive scientific understanding of the frequently complex and nonintuitive real-world systems that machine-learning algorithms examine. Using examples drawn from cutting-edge scientific research, we argue machine-learning algorithms can advance fundamental scientific knowledge and that deliberate secrecy around machine-learning tools restricts that learning enterprise.