By Dr. Carrie Diaz Eaton, Bates College, @mathprofcarrie

In the realm of humanistic mathematics, two important gatherings happened in April 2019. The first was an NSF INCLUDES funded conference called “Bringing the Conversation of Inclusion and Diversity in Data Science to the Ecology and Environmental Science Community,” which seeded EDSIN, the Environmental Data Science Inclusion Network.  The second was the second meeting of the Ethics and Mathematics Conference in the UK.

At first glance, one might not see the relationship between these two meetings. Perhaps for this reason, as I sought sponsors for the EDSIN conference, mathematics communities weren’t sure how they should be involved.

While Mathematics as a field includes much more than data science, MAA is still well-poised to support and promote interdisciplinary data science education. Certainly there are a lot of other disciplines poised to mold this area. This conference in particular, attracted many professionals from data science fields in Geographic Informations Systems and Bioinformatics who are serving as models for other fields to transition to using data in their teaching and research.

The EDSIN conference provided a unique space to spotlight environmental justice, the reclamation of indigenous rights, the reparations owed to Black America, and the emerging data divide. Where else in the mathematics community are we inviting these honest conversations?

At the EDSIN conference, Carolyn Finney talked honestly about what it felt to be not just underrepresented, but unheard. She was honest about the consequences in the academy - not just because of who we exclude in our discussion of environmental data, but also ways that we disempower even our own underserved faculty. How does this lack of support, which eventually can manifest into denial of tenure, further add to issues of underrepresentation in positions of power?

Often, individuals with power collect data from individuals with lesser power. Then the data can be used in such as way that it further disempowers those with lesser power and undermines those with the least resource capacity (e.g. Andrejevic 2014).  This “data divide” is a key component in the weapons of math destruction I discussed in my previous post about the Data4BlackLives movement and Cathy O’Neil’s book.

This data divide is an unstable equilibrium threshold that keeps the “haves” and the “have nots” separate. The power of data is the force that widens the distance between them in positive feedback loops that make the “rich get richer.”

As MAA positions itself as a leader in education concerned with inclusivity, we have to look at the time and spatial scales at which disparities in education operate. These scales are subject to the same systems flow that perpetuates the data divide.

Although our classrooms usually operate on a smaller scale, our entire professional body and broader mathematical community operate on these broad scales. We represent mathematicians all over the country who teach future teachers of all grades, who in turn teach future collectors of data and builders of algorithms. We are practitioners in these industries or in policy positions. We are part of these communities who are disparately affected. To what extent are we obligated to re-examine our role in making sure that data science is emerging with an eye towards ethics and social justice?

As I was mulling these big questions toward the conclusion of the EDSIN conference, I started to see the #EiM2 hashtag from my friends and fellow PRIMUS board colleagues, Dr. Victor Piercey and Dr. Catherine Buell. Several time zones away, the mathematical community was also considering the same broad ethics questions in mathematics.

As the #EDSIN2019 and #EiM2 hashtags brought a lively debate to my Twitter feed, I pondered, what happens now? Where does the math community take this up after these conferences are over?

As the MAA committee chair for minority participation in mathematics, minority participation cannot be untangled from the same flow that maintains the data divide. But this is not a problem that this committee can or should fix. The only thing that can combat systemic racism is a social movement with capacity for change. I know I am not the only mathematician with these concerns and thinking about systemic shifts. It makes me consider how the whole MAA community could be harnessed for the kind of work that needs to be done.

Could the answer be a new Data Science SIGMAA? Could it be a reorientation of the Quantitative Literacy SIGMAA? Should every program review done by an MAA representative look for evidence of ethics in the mathematics curriculum and look for equity and inclusivity in its department? Could it be a grassroots #DisruptDataScience inspired by my colleague, Dr. Harron?

I recently gave a talk as part of a graduate lecture series in Data Science for Biology at the University of Puerto Rico - Río Piedras. I was honored to be the first discipline-based education researcher that they had brought for this lecture series.  

The question came up - how do we teach this better? What do we do? At present I have no answers. But I’d like each of us to at least ask the right questions. To what extent does the work we do hold the status quo, widening the data divide? Or does our work seek to actively push against systematic marginalization of our community members? How could each MAA committee, each SIGMAA, each member of the mathematics community work to make sure that a future driven by mathematics, data, and algorithms is a just future?

I want to know, because I want to crowdsource good ideas from our community. Have you thought about the future our algorithms will shape and how are you intentionally been working to make sure it is a future inclusively designed for all of us? Have you organized sessions at national and section meetings? Are you inviting speakers that can speak to both our students and our colleagues on these issues? Are they invited as diversity talks or are they part of an effort to mainstream these discussions? Let us know!