A Letter to the Journal of Statistics and Data Science Education — A Call for Review of “OkCupid Data for Introductory Statistics and Data Science Courses” by Albert Y. Kim and Adriana Escobedo-Land

As Big Data continues to rise in popularity, so does an increased need for protection against potential misuses of data. We are a group of undergraduate Statistical and Data Science major students from Smith College that are actively engaged in ethical discussions concerning the use of data in our society. It can be challenging to predict future trends and technologies in data science that could cause concerns. However, we believe that some essential protections and procedures should be in place to help prevent misuses of data. In particular, we are writing to you to address our concerns with the article “OkCupid Data for Introductory Statistics and Data Science Courses” by Albert Y. Kim and Adriana Escobedo-Land that was published in your journal (Kim and Escobedo-Land 2015). In light of ethical concerns surrounding the article, herein we describe the background of how the dataset was found to contain identifiable information. We communicated this to the authors, who correspondingly corrected the article. In our opinion, there is no doubt that the dataset presented in the article holds pedagogical value as well as research value. One aspect of the educational value of the dataset is the fact that the context of possible analysis could better drive students’ interests. The research value of the data lies within the self-reported nature of the dataset, which usually is the private property of corporations and could be hard to obtain for researchers in universities. Another context in which the pedagogical value of the dataset remains is where students could use this as a case study in discussions of the ethical implications of such data, even practicing anonymization skills with the data. However, we do believe that for the dataset to be used for pedagogical purposes, further anonymizations to the dataset were necessary. Some ways that datasets like this one could be better anonymized in the future include removing unimportant variables that have identification power disproportionate to their value to research. For example, in the case of the OkCupid dataset associated with the paper, the time the data was collected could be removed, since this fact is not particularly essential but can be used for identification. Other sources of concern for this dataset are the variables that reveal geographical and temporal information on individuals. Another method could