344 Trkic G00gle: Why and How Users Game Translation Algorithms SOOMIN KIM, Seoul National University, South Korea CHANGHOON OH, Boston College, USA WON IK CHO, Seoul National University, South Korea DONGHOON SHIN, Seoul National University, South Korea BONGWON SUH, Seoul National University, South Korea JOONHWAN LEE ∗ , Seoul National University, South Korea Individuals interact with algorithms in various ways. Users even game and circumvent algorithms so as to achieve favorable outcomes. This study aims to come to an understanding of how various stakeholders interact with each other in tricking algorithms, with a focus towards online review communities. We employed a mixed-method approach in order to explore how and why users write machine non-translatable reviews as well as how those encrypted messages are perceived by those receiving them. We found that users are able to fnd tactics to trick the algorithms in order to avoid censoring, to mitigate interpersonal burden, to protect privacy, and to provide authentic information for enabling the formation of informative review communities. They apply several linguistic and social strategies in this regard. Furthermore, users perceive encrypted messages as both more trustworthy and authentic. Based on these fndings, we discuss implications for online review community and content moderation algorithms. CCS Concepts: · Human-centered computing → User studies. Additional Key Words and Phrases: Human-AI Interaction; algorithmic experience; gaming; translation algorithm; online review; recommendation algorithm; peer-to-peer platform ACM Reference Format: Soomin Kim, Changhoon Oh, Won Ik Cho, Donghoon Shin, Bongwon Suh, and Joonhwan Lee. 2021. Trkic G00gle: Why and How Users Game Translation Algorithms. Proc. ACM Hum.-Comput. Interact. 5, CSCW2, Article 344 (October 2021), 24 pages. https://doi.org/10.1145/3476085 1 INTRODUCTION People interact with algorithms in various ways as AI has increasingly found its way into our daily lives. YouTube users constantly watch video clips as the curation algorithm suggests, and Amazon consumers often add the products that it suggests. Furthermore, users attempt to actively engage with such algorithms. For example, when users are facing issues with curation or recommendation ∗ Corresponding author Authors’ addresses: Soomin Kim, Seoul National University, South Korea, soominkim@snu.ac.kr; Changhoon Oh, Boston College, USA, changhoon.oh@bc.edu; Won Ik Cho, Seoul National University, South Korea, tsatsuki@snu.ac.kr; Donghoon Shin, Seoul National University, South Korea, ssshyhy@snu.ac.kr; Bongwon Suh, Seoul National University, South Korea, bongwon@snu.ac.kr; Joonhwan Lee, Seoul National University, South Korea, joonhwan@snu.ac.kr. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Association for Computing Machinery. 2573-0142/2021/10-ART344 $15.00 https://doi.org/10.1145/3476085 Proc. ACM Hum.-Comput. Interact., Vol. 5, No. CSCW2, Article 344. Publication date: October 2021.