Research & Publications
Dating through the filters, Social Philosophy and Policy, forthcoming
I explore ethical considerations that might arise from the use of collaborative filtering algorithms in dating apps. Collaborative filtering algorithms can predict the preferences of a target user by looking at the past behavior of similar users. By recommending products through this process, they can influence the news we read, the movies we watch, and more. They are extremely powerful and effective on platforms like Amazon and Google. Recommender systems on dating apps are likely to group people by race, since they exhibit similar patterns of behavior: users on dating platforms seem to segregate themselves based on race, exclude certain races from romantic and sexual consideration (except their own), and generally show a preference for white men and women. As collaborative filtering algorithms learn from these patterns to predict preferences and build recommendations, they can homogenize the behavior of dating app users and exacerbates biased sexual and romantic behavior.
Virtual competitions and the gamer's dilemma, Ethics and Information Technology, 2020
Morgan Luck’s gamer’s dilemma (Luck 2009) rests on our having diverging intuition when considering virtual murder and virtual child molestation in video games. Virtual murder is seemingly permissible, when virtual child molestation is not and there is no obvious morally relevant difference between the two. I look into competitive games, to expand Rami Ali's dissolution of the dilemma (Ali 2015): I argue that when competitors consent to participate in a competition, the rules of the competition supersede everyday moral intuitions. Virtual children cannot be represented as giving consent to be molested because (1) children cannot be represented as giving sexual consent, and (2) consent to be possibly molested cannot be given. This creates a morally relevant difference between murder and molestation.
Human-AI Teaming for Big Data Analytics to Enhance Response to the COVID-19 Pandemic: I am currently working with a research team who received a National Science Foundation RAPID/ Collaborative grant to study the process of real-time decisions that digital volunteers make when quickly converting social media data into codes for machine learning. This will allow us to better the human-machine teaming process. Here is the link that gives all the details of the grant.
Online-Computer-Mediated Interviews and Observations: Overcoming Challenges and Establishing Best Practices in a Human-AI Teaming Context, Proceedings of the 54rd Annual Hawaii International Conference on Social Systems, 2021 (with with Keri Stephens, Amanda Hughes, Anastazja Harris, Caroline Montagnolo, Ashley Stevens, Yasas Senarath, and Hemant Purohit)
This study, conducted during early phases of COVID-19, included 15 volunteer tweet-annotators working with an emergency response organization. This method contribution uses cues-related and surveillance theories to reveal challenges and best practices when asking research participants to share their screen, be on video, and participate in a multiple-interview study. The findings suggest that researchers conducting online-mediated research should be prepared to provide technical support for the devices and interfaces participants use during the study, find ways to “see” beyond what is on the mediated screen, and consider ethical issues not often discussed.
UT Austin Graduate Student Assembly Travel Award, Spring 2021
$500 - Travel award to present my work on algorithmic matchmaking at the University of Arizona's Graduate Student Feminist Philosophy Conference
Vice-President for Research Special Research Grant, Fall 2020
$900 - Research Grant to study user experience with algorithmic matchmaking on dating apps.
Association Philippe Jabr Scholarship, Fall 2020
$10,000 - Competitive university scholarship to Lebanese students in higher education.
Good Systems COVID-19 Research Graduate Student Award, Summer 2020
$3,960 - Graduate Research Funding to assist with COVID-19 research teams at UT Austin (see above)