The Commodification of Attention, Distrust and Resentment: a Threat to (Rawlsian) Justice

There is a growing body of scholarship on how AI technology can undermine democratic institutions. I present a novel contribution to this literature, by accounting for how and why recommendation algorithms for engagement optimisation undermine the necessary conditions for Rawlsian justice. For Rawls’s political theory, the ability to form bonds of trust with fellow citizens is a necessary condition for citizens to develop their sense of justice; and their sense of justice is a necessary condition for the attainment of justice. Recommendation algorithms amplify the space given to hateful, violent, extremist, false, and discriminatory content, I argue. This content undermines the development of mutual trust between citizens necessary for a sense of justice. If citizens can only trust their like-minded members and have a distrust of their fellow citizens, then the possibility for Rawlsian reciprocity in liberal society to be realised is not possible. Without reciprocity, liberal political systems will be inherently unstable, as citizens would not have formed the adequate affectionate ties needed for mutual cooperation, which is a precondition for a just society.

Authors

Institutions

Publication Details

Journal
Technology and Regulation
Published
2025-07-15
DOI
https://doi.org/10.71265/k6r46952
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Commodification of Attention, Distrust and Resentment: a Threat to (Rawlsian) Justice

Paige Benton
Technology and Regulation
Ethics and Social Impacts of AI
article

The Commodification of Attention, Distrust and Resentment: a Threat to (Rawlsian) Justice

Paige Benton
article en

Abstract

There is a growing body of scholarship on how AI technology can undermine democratic institutions. I present a novel contribution to this literature, by accounting for how and why recommendation algorithms for engagement optimisation undermine the necessary conditions for Rawlsian justice. For Rawls’s political theory, the ability to form bonds of trust with fellow citizens is a necessary condition for citizens to develop their sense of justice; and their sense of justice is a necessary condition for the attainment of justice. Recommendation algorithms amplify the space given to hateful, violent, extremist, false, and discriminatory content, I argue. This content undermines the development of mutual trust between citizens necessary for a sense of justice. If citizens can only trust their like-minded members and have a distrust of their fellow citizens, then the possibility for Rawlsian reciprocity in liberal society to be realised is not possible. Without reciprocity, liberal political systems will be inherently unstable, as citizens would not have formed the adequate affectionate ties needed for mutual cooperation, which is a precondition for a just society.

Technology and RegulationVol. 2025
University of Johannesburg (ZA)
University of Johannesburg, Universiteit van Tilburg
Openalex Percentile: Top 69%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.