Creating Transparency and Fairness in Automated Decision Systems for Administrative Agencies

Author: Kevin Wei


Artificial intelligence is increasingly being used to make decisions about human welfare. Automated decision systems (ADS) administer U.S. social benefits programs—such as unemployment and disability benefits—across local, state, and Federal governments. While ADS have the potential to enable large gains in efficiency, they also run a high risk of reinforcing the class- and race-based inequities of the status quo. Additionally, the use of these systems is not transparent, often leaving individuals with no meaningful recourse after a decision has been made. Individuals may not even know that ADS played a role in the decision-making process.

The Federal Government should take immediate action to promote the transparency and accountability of automated decision systems. Agencies must build internal technical capacity as well as data cultures centered around transparency, accountability, and fairness. The White House should require that agencies using ADS undertake a notice-and-comment process to disclose information about these systems to the public. Finally, in the long-term, Congress must pass comprehensive legislation to implement a single, national standard regulating the use of ADS across sectors and use cases.

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About the Author

Kevin Wei is a graduate student in computer science at the Georgia Institute of Technology. His research interests include bias and fairness in machine learning and artificial intelligence, content regulation on the Internet, and the social impacts of technology. In addition to his academic work, he is an activist based in New York City.