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In the last two years, the U.S. government has emphasized the importance ofaccelerating artificial intelligence (AI) and machine learning (ML) within thegovernment and across the nation. In particular, the National ArtificialIntelligence Initiative Act of 2020, which became law on January 1, 2021,provides for a coordinated program across the entire federal government toaccelerate AI research and application. The U.S. government can benefit frompublic artificial intelligence and machine learning challenges through thedevelopment of novel algorithms and participation in experiential training.Although the public, private, and non-profit sectors have a history ofleveraging crowdsourcing initiatives to generate novel solutions to difficultproblems and engage stakeholders, interest in public competitions has waned inrecent years as a result of at least three major factors: (1) a lack ofhigh-quality, high-impact data; (2) a narrow engagement focus on specializedgroups; and (3) insufficient operationalization of challenge results. Herein weidentify common issues and recommend approaches to increase the effectivenessof challenges. To address these barriers, enabling the use of publiccompetitions for accelerating AI and ML practice, the U.S. government mustleverage methods that protect sensitive data while enabling modelling, enableeasier participation, empower deployment of validated models, and incentivizeengagement from broad sections of the population.

Advancing Artificial Intelligence and Machine Learning in the U.S. Government Through Improved Public Competitions