  In the last two years, the U.S. government has emphasized the importance of
accelerating artificial intelligence (AI) and machine learning (ML) within the
government and across the nation. In particular, the National Artificial
Intelligence Initiative Act of 2020, which became law on January 1, 2021,
provides for a coordinated program across the entire federal government to
accelerate AI research and application. The U.S. government can benefit from
public artificial intelligence and machine learning challenges through the
development of novel algorithms and participation in experiential training.
Although the public, private, and non-profit sectors have a history of
leveraging crowdsourcing initiatives to generate novel solutions to difficult
problems and engage stakeholders, interest in public competitions has waned in
recent years as a result of at least three major factors: (1) a lack of
high-quality, high-impact data; (2) a narrow engagement focus on specialized
groups; and (3) insufficient operationalization of challenge results. Herein we
identify common issues and recommend approaches to increase the effectiveness
of challenges. To address these barriers, enabling the use of public
competitions for accelerating AI and ML practice, the U.S. government must
leverage methods that protect sensitive data while enabling modelling, enable
easier participation, empower deployment of validated models, and incentivize
engagement from broad sections of the population.
