Content Filtering with Inattentive Information Consumers
Abstract
We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content. Motivating examples include censoring misinformation, spam/phish filtering, and recommender systems acting on a stream of content. When the attacker is exogenous, we show that improving the filter’s quality is weakly Pareto improving, but has no impact on equilibrium payoffs until the filter becomes sufficiently accurate. Further, if the filter does not internalize the consumer’s information costs, its lack of commitment power may render it useless and lead to inefficient outcomes. When the attacker is also strategic, improvements in filter quality may decrease equilibrium payoffs.
Cite
Text
Ball et al. "Content Filtering with Inattentive Information Consumers." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I9.28803Markdown
[Ball et al. "Content Filtering with Inattentive Information Consumers." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/ball2024aaai-content/) doi:10.1609/AAAI.V38I9.28803BibTeX
@inproceedings{ball2024aaai-content,
title = {{Content Filtering with Inattentive Information Consumers}},
author = {Ball, Ian and Bono, James W. and Grana, Justin and Immorlica, Nicole and Lucier, Brendan and Slivkins, Aleksandrs},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {9485-9493},
doi = {10.1609/AAAI.V38I9.28803},
url = {https://mlanthology.org/aaai/2024/ball2024aaai-content/}
}