What Is Data Equity And Equitable Data Science?
for higher math like Calculus. It goes by other names too like data equity, sometimes educators simply call it data science, when they aren't really referring to traditional data science so much as Equitable Data Science. So what exactly is Equitable Data Science? What follows is everything you need to know.
Equitable Data Science is one of the new buzz words being thrown around in education circles. You may have seen it in the curriculum for your child’s school, sometimes even as a replacement for higher math like Calculus. It goes by other names too like data equity, sometimes educators simply call it data science, when they aren’t really referring to traditional data science so much as Equitable Data Science. So what exactly is Equitable Data Science? What follows is everything you need to know.
Activists and advocates in the college system have been lobbying to change the way education is done, claiming the math systems that we rely on data to create and report may have racial implications. To solve this perceived problem, they’re pushing for a new approach called “Data equity” or equitable data science. Equitable Data Science is a set of ideas that are both complex and multi-faceted. They look at ways in which data is collected, studied, interpreted, and then sent out, all through an “equity” lens.
First, it might help to define the meaning of the word “equity”. Equity is often confused with “equality” but in reality the two terms have little in common. Put as simply as possible, “equality” means everyone gets the same starting point and what happens after that is up to them. On the other hand, “equity” means that regardless of how you start out, everyone should end up in the same place. So now let’s put it all these words together and find out what “equitable” data science actually is.
Equitable Data Science Defined
Proponents of this new Equitable Data Science way of thinking claim that due inequality some communities that may not have the same access to data as others. Because of this believed inequality, race activists believe there is harm caused by data misuse.
So their goal is for data equity to force students to take a closer look at how data can add what they see as “problems” to issues like racial bias or reinforcing negative stereotypes. For example, while the actual data shows that more white people are killed by cops than black people, they believe these facts obscure the problems that black people uniquely face when dealing with the police. In a world driven by equitable data science, stats like that would be ineligible for consideration.
The dedication to this way of thinking among academia has resulted in a flood of schools implementing data equity in their curriculum. Educators insist that to make math more equitable in middle schools and high schools, equitable data science needs to be at the forefront of teaching. On the flipside, they’re eliminating other forms of math on the grounds that studying something like calculus may result in bias.
Proponents of data equity believe that every level of math holds a racial and socioeconomic disparity. Equitable data science in their view would level the playing field. Instead of using calculus to identify racial inequalities, data science would ensure that everyone has what they think is a fair shot.
How Data Equity Works In The Real World
So does Equitable Data Science work? Is data equity even possible? The fundamental principle here is that EDS supporters believe data is not objective. They say that while it is true that numbers by themselves are neutral, those who collect data, who interpret it, analyze it, and disseminate it, bring to it their own experiences and possible biases. Potential goals, questions and framing can be skewed by our differing perspectives, even if unintended.
One way of getting from here to there is broken down by data equity advocates into seven stages. These stages are referred to as the data life cycle. We All Count explains that these seven stages can offer a better commitment to equity, fairness, and access to data.
The 7 Stages Of The Data Life Cycle
- Funding
- Motivation
- Project Design
- Data Collection & Sourcing
- Analysis
- Interpretation
- Communication & Distribution
The idea behind thes seven stages is to break up your data work into workable parts, turning it into an equity-oriented process. This goes not only for businesses across the nation but also for how we are looking to reinvent the school education wheel. And that’s Equitable Data Science.
A clear example of how equitable data science came into play happened in St. Paul, Minnesota. For over a year a group of diverse youths and their community fought to stop the school district’s effort to screen their children for risk of juvenile delinquency. The district was using zip codes, truancy data, family income, and race to parse out possible troubled youths. Over that year, advocates of the community were able to use the data found to show the district had a long history of disciplining students of color, thereby successfully arguing for the analytics project to stop.
While Equitable Data Science is no longer controversial in academia, it is nearly everywhere else. Opponents cite it as a component of Critical Race Theory, which has been strongly opposed by parents everywhere. Despite the objections of many parents, data equity is quickly spreading throughout public and private schools in America. Check your child’s curriculum closely, if you want to know whether it’s being taught to them.
