Automated decisions are increasingly part of everyday life, but how can the public scrutinize, understand, and govern them? This report from Upturn and the Omidyar Network maps out the landscape, providing practical examples and a framework to think about what has worked.
Our key findings in this report are:
Today’s automated decisions are socio-technical in nature: They emerge from a mix of human judgment, conventional software, and statistical models. The non-technical properties of these systems — for example, their purpose and constraining policies — are just as important, and often more important, than their technical particulars. Automated systems vary in their goals and design, and demand different kinds of inquiry.
Scrutiny doesn’t have to be sophisticated to be successful. Many of the most notable case studies we identified involved investigative reporting and basic observation of a system’s purpose, policies, inputs and outputs. Such approaches have led to productive public attention. However, more technically sophisticated types of scrutiny are beginning to bear fruit, especially in the realm of “black box testing.”
There are promising new methods for designing more accountable systems, but these remain largely theoretical. Researchers are working hard on new ways to detect bias in datasets, to design predictive models that are interpretable, and to verify the behavior of important software. However, these techniques — many of which require proactive cooperation from institutions of interest — are likely to remain in the lab until civil society makes a clearer case for their adoption.
Many existing legal and regulatory frameworks remain relevant to automated systems’ behavior and output, but their applicability is often unclear or untested. Some laws have been recently updated to specifically address automated decisions, but they remain largely untested. Others may require updating to remain effective in the era of widespread automation.