Datamatch's New Direction

It is time for the survey to match its algorithm to the student body.

Valentine’s Day for Harvard students can be unique because of our anticipation for the Harvard Computer Society’s annual matchmaking survey Datamatch. The program runs an algorithm based on survey questions and biographical data to match undergraduate students with potential dates for food around Harvard Square. However, the program is facing criticism by students due to its adherence to the gender binary in introductory questions for participants.

Contention focuses on Datamatch's requirement that an individual to declare their gender and the preferred gender of potential matches within the limited categories of male or female. Individuals who wish to include more information about their gender identity outside of this binary can only do so in a section marked “extra” that was originally placed at the end of the survey.

A binary conceptualization of gender does not leave space for the representation of individuals whose gender identity falls out of the binary. Forcing students to choose a gender identity that is restricted to two fields centers cisgendered norms and erases the multiplicity of nonconforming gender identities.

We feel that continued use of a gender binary on the survey demonstrates an oversight regarding the diversity of our student population. Datamatch’s constrictions show the shortcomings of current pushes to make Harvard a more inclusive space for all identities. Even when it comes to popular, light-hearted traditions like Datamatch, the entire student body should feel able to participate. While the program is well intentioned in its aim to foster friendship and romance, the focus on specific identities and alienation of others is unacceptable.

That said, we acknowledge and appreciate the respectful actions of the developers and students working on the program to improve it for the future. Since the release of the survey, the developers have generated productive dialogue with students passionate about inclusion on how to improve Datamatch.

This is an issue requiring cooperative feedback from Harvard students of various experiences and passions. In the future, we hope Datamatch will be more cognizant of the impact of its survey on student inclusion. We understand that the mystery of the algorithm adds to the overall experience of Datamatch; however, transparency is necessary when it comes to nuanced issues like these that may require feedback from students outside of HCS. Inclusivity should not be sacrificed for mystery.

We also understand that this is both a highly technical issue and a complicated social issue. This situation provides the opportunity for valuable interdisciplinary collaboration between a more diverse group of Harvard students. The program’s dependence on a binary gender field must be expanded in some way, possibly through creating a third, gender-nonconforming option and including an option to receive gender-conforming and gender-nonconforming matches. We encourage Harvard students of all disciplines to continue to brainstorm new approaches to this issue and remind Datamatch developers that this is not an impossible problem.


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