Nicholas Diakopoulos

Nicholas Diakopoulos is a computer scientist and journalism scholar whose research has defined the field of algorithmic accountability. He is Professor of Communication Studies and Computer Science at Northwestern University, where he directs the Computational Journalism Lab in the Medill School of Journalism. His work investigates how algorithmic systems shape public life and how journalism can hold those systems accountable through investigation, audit, and reporting.

Diakopoulos earned his PhD in computer science from the Georgia Institute of Technology, where he studied at the intersection of natural language processing and human-computer interaction. He has held positions at Rutgers, the University of Maryland, and as a Tow Fellow at the Tow Center for Digital Journalism at Columbia University. His research draws on data science, computational methods, and traditional reporting practice to investigate decision-making by algorithmic systems in domains ranging from criminal justice to elections to social media.

His 2014 Tow Center report Algorithmic Accountability Reporting: On the Investigation of Black Boxes established a methodological vocabulary for journalists investigating consequential algorithmic decisions, including reverse engineering, transparency requests, and audits of input-output behavior. The report has been widely cited as the founding text of the algorithmic accountability beat, and the methods it proposed have been used to investigate algorithms used in policing, hiring, content moderation, and political advertising.

His 2019 book Automating the News: How Algorithms Are Rewriting the Media extended the analysis to journalism's own use of automated systems, including news recommendation, automated fact checking, and AI-generated content. The book examines both the benefits and the editorial responsibilities that accompany algorithmic decision-making inside news organizations.

Diakopoulos's research is funded by the National Science Foundation, the Knight Foundation, the Tow Foundation, and others. His framework for algorithmic accountability has been adopted by journalism schools, civic technology projects, and regulatory bodies internationally. He continues to publish on the operational details of investigating AI systems in the public interest, and his ongoing work has expanded into the question of how generative AI is reshaping the production and verification of news itself, a question that puts the same accountability discipline he developed for external algorithms in front of journalism organizations applying AI in their own newsrooms.

Nicholas Diakopoulos's work grounds AI Inherits Messiness. The treatise cites his 2015 Digital Journalism paper for the concept of "algorithmic accountability," the principle that designers and deployers of AI systems must acknowledge and take responsibility for the value judgments embedded in those systems.

This is the philosophical foundation underneath the principle's core directive to define what's prohibited over what's required. Diakopoulos's contribution is the recognition that algorithmic decisions are not neutral. They encode the priorities, assumptions, and blind spots of the people who design them and the data they are trained on. Treating those systems as if they were neutral is itself a value judgment, one that shifts moral responsibility off the designer and onto the algorithm. His research provides the methodological vocabulary the treatise relies on when it asks what happens when no one is named the owner of an AI system's outputs.