Helping journalists understand the functions, impacts, and ethics of algorithms

Read More 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.

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.

Algorithmic Accountability

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Algorithmic accountability reporting adapts journalism’s traditional watchdog function to investigate algorithms, their social impacts beyond the world of tech, and their potential for perpetuating existing biases, inequalities, and other social risks. These three sections are intended primarily for journalists who encounter algorithms in the course of doing their jobs or who are interested in reporting on them.

Relevant Questions

What to ask—your sources and yourself

Newsworthy Sources

Who to ask—Expanding the scope of authorized sources

Informative Frames

How to avoid seven common pitfalls in AI reporting

Algorithmic "Gatekeeping"

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Algorithmic “gatekeeping” restricts journalists and newsrooms from reaching a wider audience through shadow bans, demonetization, and other forms of content reduction. The sections on shadow bans and advertising “blocklists” are intended for newsrooms managers, while journalists and managers may be interested in algorithm audits.

Conducting a DIY Algorithmic Audit

Six steps to produce newsworthy findings that promote algorithmic accountability

Responding to Shadow Bans

Five tips for recognizing and responding to shadow bans of news content

Sidestepping Advertising "Blocklists"

Nine guidelines help newsrooms protect their bottom line while also informing their audiences.

Published Articles

For more information on this project, check out the Algorithmic Literacy for Journalists’ published posts, written by Andy Lee Roth, with contributions from avram anderson and Shealeigh Voitl, and published on the Reynolds Journalism Institute website.

Picture of the ALFJ Homepage

Introducing Algorithmic Literacy for Journalists

Resources to help journalists and newsrooms confront power imbalances by promoting industry accountability and public understanding

February 24th, 2025

System Error

Learning from a newspaper’s plan to deploy an AI-powered “bias meter”

February 4th, 2025

Life preserver on a ship deck behind storm clouds.

Resources to support newsrooms’ responsible use of and reporting on algorithms

An annotated directory of how-to guides, exemplary journalism and policies — plus a cool online game

January 7th, 2025

Close up of feet walking a highwire

How advertising blocklists undermine online journalism

Nine guidelines help newsrooms protect their bottom line while also informing their audiences

November 25th, 2024

Jockeying for attention

Lessons from “horse race” coverage can improve reporting on new developments in AI technology

October 30th, 2024

Recognizing and responding to shadow bans

Social media restrictions can render newsworthy reporting on controversial topics invisible

September 30th, 2024

How to conduct a DIY algorithm audit

Six steps to produce newsworthy findings that promote algorithmic accountability

September 4th, 2024

Big tech algorithms: The new gatekeepers

Algorithmic Literacy for Journalists will be an interactive resource to help journalists understand the functions, consequences and ethics of algorithms in a digital age

July 31st, 2024

Our Team

Andy Lee Roth

Project Lead

(he/him)

avram anderson

Researcher/Author

(they/them)

Kate Horgan

Website Design Lead

(she/they)

Adam Armstrong

Project Administrator

(he/him)

Shealeigh Voitl

Researcher/Author

(she/her)

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