The Task Force on Diversity, Equity, and Inclusion (DEI) in the mathematical sciences has been reflecting on how our department can contribute to MSU’s DEI goals. It is easy to assume that mathematics is an objective endeavor independent of human subjectivity, but people do mathematics and our discipline thrives when all people are welcomed into our community. 

 

A commitment to “advance the status of the profession of mathematics, encouraging and facilitating the full participation of all individuals” is made explicit in the mission statement of the American Mathematical Society, though we recognize that our discipline has not always lived up to that ideal. Myths about what it means to know and do mathematics exist both within and outside the field and act as barriers to the full participation of all individuals.  

 

Many of us have heard the joke, “There are three kinds of people in this world – those who are good at math and those who aren’t.” While this statement is likely to draw a chuckle, the notion that success in mathematics is the result of some spark of brilliance, rather than sustained hard work, pervades Western society and is more than a harmless myth (Anderson, R. K., Boaler,J., & Dieckmann, J. A. (2018)).  Disciplines that value or are seen as requiring “brilliance” have the starkest disparities in participation by women and people of color, and mathematics is at the top of that pile (Leslie, S. J., Cimpian, A., Meyer, M., & Freeland, E. (2015)).   

 

Members of the DEI task force in the mathematical sciences have been asking ourselves how we value diversity, equity, and inclusion in our discipline and where we need to grow and change. Though our work is ongoing, the answers we are developing are both encouraging and challenging. For one, both mathematics and statistics welcome different ways of approaching problems, a value that readily extends to a variety of perspectives and people doing math. Also, mathematical abstraction lends itself to, and is born from, applications. A more diverse community of mathematicians and statisticians broadens the range of priorities represented by those developing and applying methodology, resulting in more innovative approaches that are responsive to a wider range of societal needs. We also recognize that mathematical and statistical models can disguise biased values and propagate inequity (Lum, K. and Isaac, W. (2016)). Indeed, a typical first step in developing a model is deciding which parameters of a system are the salient ones to measure.  

 

Correspondingly, through our teaching, mentorship, and curriculum design, we see several opportunities.  One is to diversify imagination of where to find mathematics and how it can be used as a tool for identifying and addressing societal issues.  Another opportunity is to disseminate a practice of being upfront about applying mathematics to a broader range of contexts, and the values and social consequences embedded in them.  Another opportunity yet is to align the standard practices in our curriculum with priorities of communities seeking to embrace mathematical tools.  

 

Here is an example of such an opportunity.

 

A Typical Linear Algebra Problem. 

There are two brands of shoes – brand A and brand B. People buy shoes every 6 months: 70% stick with brand A; 30% switch to brand B; 80% stick with brand B; 20% switch to brand B. For a given shoe store, each pair of shoes not sold is a sunk cost.  Among the 500 pairs of shoes that the store can carry in stock, how many should be brand A and how many should be brand B in order to maximize profit? 

Much of this context is not relevant to the mathematical essence of this problem.  Meanwhile, the context of this problem sets an imagination that mathematics can be used as a tool for maximizing profit – a value that we readily accept under the guise of innocuous.  In the spirit of shifting imagination to social issues, here is the same problem – through the lens of mathematics – yet premised on a different set of parameters and implied values. 

Modified Linear Algebra Problem. 

There are two rental neighborhoods in Bozeman – the North and the South. Renters renew their leases every 6 months: 70% stick with the North; 30% switch to the South; 80% stick with the South; 20% switch to the North. The city of Bozeman injects neighborhood-wide affordable housing incentives based on concentration of renters. Among the 500 incentives the city has to available, how many should be offered to the North and how many to the South, in order to optimize the mission of the incentives?  

 

We know we have a long way to go. Inequities within the mathematical sciences did not appear overnight. They are the result of centuries of discriminatory practices and new wounds are being inflicted on historically underrepresented groups to this day. We are encouraged by efforts within and across our disciplinary societies (e.g., the American Mathematical Society, the American Statistical Association, the Association of Mathematics Teacher Educators) and by steps taken to support diversity and inclusion across MSU’s campus. We look forward to creating a more equitable, inclusive, and just discipline that lives up to the ideal of the full participation of all individuals.