Without Adequate Representation Data Science May Threaten Queer PeopleJun 26, 2023
In a continued pride month discussion we review comments from Os Keyes, gender studies expert - a data ethics researcher - and tech guru who has written provocatively written about how data science threatens the safety queer lives.
The nature and existence of this threat is because data science requires converting raw reality into numbers which many find difficult when their identity cannot easily slot into existing categories like "female" or “male."
How Data Science Threatens To Erase Queer Lives
Data science relies on numbers to understand the world. Creating numerical representations might be helpful in some cases, but it is limiting in others. For example, imagine why data science would have a difficult time understanding the complexities of human emotions. How do you quantify emotion?
Additionally, data science often relies on pre-existing categories to make sense of the world. However, this categorization can be problematic for queer people because we do not always fit neatly into pre-existing categories.
For example, let’s say a data scientist wanted to study the relationship between gender and employment. The scientist might look at government data sets that track employment by sex. However, this data would only be accurate if everyone fell neatly into one of two categories: male or female. However, there are many people who do not identify as either male or female. As a result, this data would erase the experiences of these people and paint an inaccurate picture of reality.
Data science has revolutionized our understanding of the world around us. However, we must be careful not to rely too heavily on data-driven approaches to understanding reality. Or at least we must remain open to other qualitative supplements.
The need to remain open, in these ways, is because data science has its limitations. We must be aware of these limitations in order to avoid erasing the experiences of queer people—or any other group that doesn’t fit neatly into preexisting categories—from our understanding of the world around us.