TY - GEN
T1 - An agent-based model to evaluate interventions on online dating platforms to decrease racial homogamy
AU - Ionescu, Stefania
AU - Hannák, Anikó
AU - Joseph, Kenneth
N1 - Publisher Copyright: © 2021 ACM.
PY - 2021/3/3
Y1 - 2021/3/3
N2 - Perhaps the most controversial questions in the study of online platforms today surround the extent to which platforms can intervene to reduce the societal ills perpetrated on them. Up for debate is whether there exist any effective and lasting interventions a platform can adopt to address, e.g., online bullying, or if other, more far-reaching change is necessary to address such problems. Empirical work is critical to addressing such questions. But it is also challenging, because it is time-consuming, expensive, and sometimes limited to the questions companies are willing to ask. To help focus and inform this empirical work, we here propose an agent-based modeling (ABM) approach. As an application, we analyze the impact of a set of interventions on a simulated online dating platform on the lack of long-term interracial relationships in an artificial society. In the real world, a lack of interracial relationships are a critical vehicle through which inequality is maintained. Our work shows that many previously hypothesized interventions online dating platforms could take to increase the number of interracial relationships from their website have limited effects, and that the effectiveness of any intervention is subject to assumptions about sociocultural structure. Further, interventions that are effective in increasing diversity in long-term relationships are at odds with platforms' profit-oriented goals. At a general level, the present work shows the value of using an ABM approach to help understand the potential effects and side effects of different interventions that a platform could take.
AB - Perhaps the most controversial questions in the study of online platforms today surround the extent to which platforms can intervene to reduce the societal ills perpetrated on them. Up for debate is whether there exist any effective and lasting interventions a platform can adopt to address, e.g., online bullying, or if other, more far-reaching change is necessary to address such problems. Empirical work is critical to addressing such questions. But it is also challenging, because it is time-consuming, expensive, and sometimes limited to the questions companies are willing to ask. To help focus and inform this empirical work, we here propose an agent-based modeling (ABM) approach. As an application, we analyze the impact of a set of interventions on a simulated online dating platform on the lack of long-term interracial relationships in an artificial society. In the real world, a lack of interracial relationships are a critical vehicle through which inequality is maintained. Our work shows that many previously hypothesized interventions online dating platforms could take to increase the number of interracial relationships from their website have limited effects, and that the effectiveness of any intervention is subject to assumptions about sociocultural structure. Further, interventions that are effective in increasing diversity in long-term relationships are at odds with platforms' profit-oriented goals. At a general level, the present work shows the value of using an ABM approach to help understand the potential effects and side effects of different interventions that a platform could take.
KW - Agent-based modeling
KW - Dating platforms
KW - Racism
KW - Social media
UR - https://www.scopus.com/pages/publications/85102616570
U2 - 10.1145/3442188.3445904
DO - 10.1145/3442188.3445904
M3 - Conference contribution
T3 - FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
SP - 412
EP - 423
BT - FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
PB - Association for Computing Machinery, Inc
T2 - 4th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2021
Y2 - 3 March 2021 through 10 March 2021
ER -