Artificial Intelligence and Democracy's Information Problem
DOI:
https://doi.org/10.5195/lawreview.2025.1095Abstract
Democracy in America faces a fundamental contradiction that threatens its future. Elected officials do not respond to the wants and needs of most Americans, yet they are being reelected at historically high rates. That contradiction has fueled rising dissatisfaction with democracy and support for its more autocratic alternatives.
To date, legal scholars have neglected a critical source of that accountability deficit: democracy’s information problem. Most democratic citizens lack the capacity or competency to acquire and process information about the policies their elected officials support or oppose and how those policy choices impact their lives. Theoretically, people may draw on increased education and use information shortcuts like party labels to ameliorate their lack of policy knowledge. But the United States’ widening education disparities and partisan polarization undermines those strategies. As a result, citizens cannot effectively hold politicians to account for decisions that undermine their well-being.
Democracy desperately needs another tool to respond to its information problem, or it may not survive. In this Article drawn from my Constitution Day Lecture at the University of Pittsburgh School of Law, I argue that artificial intelligence (“AI”) can serve as that tool. While many consider AI to be a threat to democracy, it is a technology uniquely capable of making democratic information easy to acquire and process. If properly developed, AI can help democratic citizens hold elected officials accountable for the policy decisions they make. For AI to effectively mitigate democracy’s information problem, though, it must be a trusted source of information. To avoid AI’s potential perils, we should utilize the principles of “trustworthy AI” to build a legal and institutional framework that conserves the democracy-promoting uses of the technology.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Bertrall L. Ross II

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
- Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons 4.0 License (Attribution-Noncommercial-No Derivative Works), or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
- Noncommercial—other users (including Publisher) may not use this Work for commercial purposes;
- No Derivative Works—other users (including Publisher) may not alter, transform, or build upon this Work,with the understanding that any of the above conditions can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work. Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- the Work is the Author’s original work;
- the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- the Work is not pending review or under consideration by another publisher;
- the Work has not previously been published;
- the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- the Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.
