TY - GEN
T1 - How algorithms shape the distribution of political advertising
T2 - 5th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2022
AU - Papakyriakopoulos, Orestis
AU - Tessono, Christelle
AU - Narayanan, Arvind
AU - Kshirsagar, Mihir
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/7/26
Y1 - 2022/7/26
N2 - Online platforms play an increasingly important role in shaping democracy by influencing the distribution of political information to the electorate. In recent years, political campaigns have spent heavily on the platforms' algorithmic tools to target voters with online advertising. While the public interest in understanding how platforms perform the task of shaping the political discourse has never been higher, the efforts of the major platforms to make the necessary disclosures to understand their practices falls woefully short. In this study, we collect and analyze a dataset containing over 800,000 ads and 2.5 million videos about the 2020 U.S. presidential election from Facebook, Google, and TikTok. We conduct the first large scale data analysis of public data to critically evaluate how these platforms amplified or moderated the distribution of political advertisements. We conclude with recommendations for how to improve the disclosures so that the public can hold the platforms and political advertisers accountable.
AB - Online platforms play an increasingly important role in shaping democracy by influencing the distribution of political information to the electorate. In recent years, political campaigns have spent heavily on the platforms' algorithmic tools to target voters with online advertising. While the public interest in understanding how platforms perform the task of shaping the political discourse has never been higher, the efforts of the major platforms to make the necessary disclosures to understand their practices falls woefully short. In this study, we collect and analyze a dataset containing over 800,000 ads and 2.5 million videos about the 2020 U.S. presidential election from Facebook, Google, and TikTok. We conduct the first large scale data analysis of public data to critically evaluate how these platforms amplified or moderated the distribution of political advertisements. We conclude with recommendations for how to improve the disclosures so that the public can hold the platforms and political advertisers accountable.
KW - accountability
KW - algorithmic auditing
KW - algorithmic targeting
KW - interpretability
KW - political advertising
KW - political speech
KW - regulation
UR - http://www.scopus.com/inward/record.url?scp=85137167546&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137167546&partnerID=8YFLogxK
U2 - 10.1145/3514094.3534166
DO - 10.1145/3514094.3534166
M3 - Conference contribution
AN - SCOPUS:85137167546
T3 - AIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
SP - 532
EP - 546
BT - AIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
PB - Association for Computing Machinery, Inc
Y2 - 1 August 2022 through 3 August 2022
ER -