Author: Matt Swayne
Publisher: Penn State
Publication Year: 2021
Summary: The following article discusses the symposium, “Harnessing the Data Revolution to Enhance Diversity,” which had the message “Data science can only be data science for good when it can be data science for all.” The symposium aimed to dissect issues and provide opportunities surrounding improving equity and diversity in data science. The article expands on how the same practices that promote fairness can perpetuate problems when used incorrectly. Unseen bias is an example of a challenge that affects AI and algorithms. These algorithms are only as good as the data that was used to build them; therefore, even unintentional biases can be perpetuated into society. However, this same data could instead be used to find critical insights into injustices in our world. The symposium emphasizes the importance of extending these issues beyond individuals, so that the future generation as a whole can improve practices in data science. Specifically, there is a need for more diverse data scientists. In addition to recruiting diverse talent, the data science space needs to be made safe, so that individuals will want to remain in the field in order to create real change.