TY - GEN
T1 - User attitudes towards algorithmic opacity and transparency in online reviewing platforms
AU - Eslami, Motahhare
AU - Vaccaro, Kristen
AU - Lee, Min Kyung
AU - Bar On, Amit Elazari
AU - Gilbert, Eric
AU - Karahalios, Karrie
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/5/2
Y1 - 2019/5/2
N2 - Algorithms exert great power in curating online information, yet are often opaque in their operation, and even existence. Since opaque algorithms sometimes make biased or deceptive decisions, many have called for increased transparency. However, little is known about how users perceive and interact with potentially biased and deceptive opaque algorithms. What factors are associated with these perceptions, and how does adding transparency into algorithmic systems change user attitudes? To address these questions, we conducted two studies: 1) an analysis of 242 users’ online discussions about the Yelp review filtering algorithm and 2) an interview study with 15 Yelp users disclosing the algorithm’s existence via a tool. We found that users question or defend this algorithm and its opacity depending on their engagement with and personal gain from the algorithm. We also found adding transparency into the algorithm changed users’ attitudes towards the algorithm: users reported their intention to either write for the algorithm in future reviews or leave the platform.
AB - Algorithms exert great power in curating online information, yet are often opaque in their operation, and even existence. Since opaque algorithms sometimes make biased or deceptive decisions, many have called for increased transparency. However, little is known about how users perceive and interact with potentially biased and deceptive opaque algorithms. What factors are associated with these perceptions, and how does adding transparency into algorithmic systems change user attitudes? To address these questions, we conducted two studies: 1) an analysis of 242 users’ online discussions about the Yelp review filtering algorithm and 2) an interview study with 15 Yelp users disclosing the algorithm’s existence via a tool. We found that users question or defend this algorithm and its opacity depending on their engagement with and personal gain from the algorithm. We also found adding transparency into the algorithm changed users’ attitudes towards the algorithm: users reported their intention to either write for the algorithm in future reviews or leave the platform.
KW - Algorithmic opacity
KW - Algorithm’s existence
KW - Algorithm’s operation
KW - Reviewing platforms
KW - Transparency
UR - http://www.scopus.com/inward/record.url?scp=85067621078&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067621078&partnerID=8YFLogxK
U2 - 10.1145/3290605.3300724
DO - 10.1145/3290605.3300724
M3 - Conference contribution
AN - SCOPUS:85067621078
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019
Y2 - 4 May 2019 through 9 May 2019
ER -