About ALFJ

Algorithms—sets of instructions for performing a task or solving a problem—are the essential building blocks for artificial intelligence (AI) systems that are revolutionizing how news is gathered and produced, distributed, and consumed. Consequently, every news worker, regardless of specialty, needs some degree of algorithmic literacy, or the ability to understand and critically evaluate algorithmic systems, and the consequences of their use.

Developed with the support of the Reynolds Journalism Institute and in consultation with experts in journalism, computer science, and media literacy, Algorithmic Literacy for Journalists (ALFJ) provides a practical toolkit to help journalists and news rooms promote public understanding of the promise, limitations, and risks of algorithmic technology in our everyday lives.

ALFJ consists of two related tool sets. The first introduces three essential components of algorithmic accountability reporting, journalism that informs the public about the risks and benefits of artificial intelligence. The second tool set arms journalists, news managers, and newsrooms whose digital content has been subject to shadowbanning, demonetization, and other forms of algorithmic “gatekeeping” that restrict them from reaching a wider audience.

By reorienting journalism’s traditional watchdog function to the domain of algorithms, ALFJ provides the resources journalists need to address how algorithms are shifting the distribution of power, not only in society at large but also within journalism.

This project is produced with support from a 2024-2025 fellowship from the Reynolds Journalism Institute.

ALFJ Team

Andy Lee Roth

Project Lead

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avram anderson

Researcher/Author

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Kate Horgan

Website Design Lead

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Adam Armstrong

Collaborator

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Shealeigh Voitl

Researcher/Author

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