I had the second class for our pilot graduate diversity & ethics class; I discussed the first class last week. I asked the students to read Leaning In: A Student’s Guide to Engaging Constructively with Social Justice Content and to review the meaning of 16 words that we can use to talk about diversity, discrimination and oppression. Since these words are new to most students (I think) and even just their meanings can be difficult to internalize, we did an exercise that I picked up in our faculty difference, power and discrimination seminar. Students were each assigned one word and wrote down a definition. Students then paired up and had 3 minutes to describe their definitions to each other. Now each student had 2 definitions. They paired up again and had 5 minutes to describe their 4 definitions to each other. They paired up a final time describe their 8 (or so) definitions. We then had an open discussion about some of the more challenging definitions (if I remember: institutional vs. structural discrimination, internalized oppression, hegemony).
I then had the students draw an identity map and to explain I showed them mine (right), and of course admitted that this is by no means complete (two glaring omissions are religion and body-type).
My goal with all this is to have students realize the privilege they are granted based on their identity in addition to simply thinking about one’s own identity.
Between talking about the diversity terminology and talking about identity, discrimination and privilege, there was a fair amount of discussion and some resistance to admitting access to privilege as well as denial of discrimination based on other identities. We talked a little bit about student resistance at the faculty DPD seminar, but I can’t say I was ready.
I remember the first classes I’ve taught. I remember the times that I got stuck in a lecture or made a mistake or was unable to explain myself. And Friday’s class wasn’t dissimilar. However, I’m a trained theoretical computer scientist, so when teaching technical material I can draw on years of experience and know that I can probably do better next time; with this graduate diversity teaching, I don’t have nearly the training. There is also a lot more emotion tied up with ideas of discrimination and privilege. It is really difficult to see yourself as perpetuating systems of oppression. It’s important to not feel blame, but I think it is important to recognize that we are all a part of these systems of oppression and we are all responsible for fighting against it. I’m not sure I did a good job of explaining that on Friday,
So, I left class with a lot on my mind and woke up the next day with it still weighing heavily. I only have 10 weeks/hours to discuss these ideas explicitly with these students and I can’t help but blame myself for not doing the best job possible. Discussions this weekend with my partner, a colleague in Philosophy and (very welcome emails) with a student in the class have helped to digest this. I have a lot to learn and I am ready to accept that the first run through of this class is not going to be perfect.
I just hope I have another opportunity to teach this in the future.
I’m curious — are you planning to discuss at all algorithmic fairness and the mechanization of bias in machine learning systems, as for instance Suresh Venkatasubramanian and Sorelle Friedler have been pushing? (See e.g. http://www.theatlantic.com/business/archive/2015/09/discrimination-algorithms-disparate-impact/403969/) It seems like an important topic for CS grads to have heard about, and a good fit for your class.
Hopefully in discussions! This line of thought would be great for a couple of the assignments the students have (to expose discrimination resulting from our research) and as part of our discussion on research ethics in a couple of weeks.
I was wondering the same thing :). But thanks for posting the link to the Leaning In guide. That should be required reading for me and others like me thinking about mechanized discrimination.
You may want to read Science and Social Inequality: Feminist and Postcolonial Issues by Sandra Harding (if you haven’t already). It questions the basic assumption that many people make: that the results of science are apolitical (and gives wonderful arguments). It might be helpful for broadening the assumptions of mechanized discrimination. I look forward to following your research on algorithmic fairness!