I have been extraordinarily blessed to have Marian Dingle join me in reading and discussing books for a #mtbos bookClub, and she has graciously written a reflection on the recent book, Weapons of Math Destruction by Cathy O’Neil. Enjoy, then follow Marian’s blog and chat with her on twitter @dingleteach
Weapons of Math Destruction: Post-Chat Thoughts
By Marian Dingle
First, I’d like to thank Annie for her work and dedication in starting and maintaining this #mtbos book chat series. I am humbled she has allowed me to share my thoughts here. I am afraid that I have far too many more questions than I have answers. But we are all here to learn together, right?
Briefly defined, a WMD (weapon of math destruction) is an algorithm that seeks to quantify certain traits in order to predict outcomes. This alone is not a new concept; we are taught to model in this way throughout our K-12 mathematical experience through algebraic relationships, calculus maximization, and even micro- and macroeconomics. What separates the modeling in WMDs is the curious ways it enters our livelihoods and the scale at which it occurs.
Initial reactions ranged from shock to validation, mixed with an urge to act.
An important point is that the author, Cathy O’Neill, a former quant who participated in creating and applying these WMDs, began as one certainly meaning no harm, but had an epiphany, ultimately leaving this lucrative field. Sherri, below, made a great point that the fact that O’Neill is female, and probably a Wall Street outsider, enabled her to see things with slightly different eyes.
Now for my (tangential?) thoughts. Of the many topics O’Neill discusses, I was struck by college selection. Although I do look at rankings, they were not much of a factor when considering college choices for my two children. As a person of color, I have learned not to solely rely on such rankings, as the information that is crucial to my family is often not captured there. Yes, I want my children to attend a “good” school, but my definition of good includes support of marginalized students, their graduation rates, and the number of faculty members of color. A brand-name university can potentially be more harmful than beneficial. This is a reality that many people of color face.
Informal algorithms like these are often generated through social networks and aid in other decisions such as where to live, work, and enroll children in K-12 settings. Would it be helpful to have more quants of color designing algorithms for big data? Perhaps, but is it even more important to control the question the algorithm seeks to answer? Would this help us with results of standardized testing? Are tests designed to justify the existence of an achievement gap? Can we design one to dismantle oppressive systems?
As we think about our roles going forward, I think it’s worth pondering our roles up to this point. More and more educators are agreeing that education and teaching, even in mathematics, is not neutral. What we choose to discuss, and not to discuss, reflects our politics, and affects our students. Do we discuss the purpose of mathematics with our students or colleagues? Have we created a space for them to discuss how mathematics can be used to support bias? Do we even ask them what they think? Do we know what we think?
What I know for sure is that we can no longer afford to be silent. Courage is required to analyze our own agendas and roles in this work.