I still haven’t posted all that much in this blog, and essentially nothing research-related. I’ve been writing a bit offline, and I’ll probably adapt some of what I’ve been thinking about into blog form fairly soon. In this post I’d like to address some issues related to sexism and gender bias in the mathematical (and perhaps broader scientific) community. I think about these issues rather often, but I’m writing about them now because of recent posts in The Accidental Mathematician (Izabella Laba’s blog) and mathbabe (Cathy O’Neil’s blog).
The thrust of Izabella Laba’s post (entitled “Gender Bias 101 for Mathematicians”) is that gender bias in the mathematical community is not limited to a few grouchy old codgers, but rather that it is a systematic cultural and psychological phenomenon which afflicts everybody. There are two potentially controversial assertions implicit in this statement:
- Gender bias in the mathematical community exists.
- Gender bias in the mathematical community is pervasive and systematic.
The first assertion is pretty hard to argue with, though I’m sure some people still try. Every math department with which I have been affiliated is *massively* male dominated, and there is ample evidence that hiring practices, salaries, journals, etc. are stacked in favor of men. I’m not going to try to document or justify this in any detail because I don’t have the facts available at my fingertips and because the issue has been argued to my satisfaction elsewhere (e.g. in the Accidental Mathematician).
The second assertion might be more surprising to some, and it’s the one I want to discuss here. Izabella Laba’s post quotes a recent study in which faculty from research oriented universities were presented with applications for a lab manager position with randomly assigned male or female names. The study found that a given application with a male name at the top was consistently rated more highly than the same application with a female name. Interestingly enough, the pattern was independent of the gender of the faculty evaluator: female professors were just as biased as male professors. Cathy O’Neil contributes another study which shows that 15-year-old girls outperform 15-year-old boys in science exams in some countries but not others (not in the United States), indicating that gender gaps in science are cultural rather than biological.
Both of these studies are quite compelling, and I’m sure there are others which point to the same conlusion. My intention is to participate in this discussion subjectively rather than objectively. In short, I am going to use the rest of this post to analyze my own gender-oriented biases. Something feels a bit self-indulgent about this exercise, but I think it will be healthy for me even if it isn’t useful for anyone else.
I will begin by admitting outright that I am biased against women. I consider myself to be a pretty progressive guy – perhaps even more progressive than most – and I think that most people who know me would say that overall I do a good job of treating women with the same respect with which I treat men. But this is not because I don’t have biases, it’s because I work very hard to identify them and eliminate them or at least minimize their impact on my behavior. I am unqualified to generalize my own psychological observations to everyone else, but I suspect that it is neurologically almost impossible for a person socialized in 20th or 21st century American society to avoid gender biases: we are bombarded with overt and covert messages about gender constantly and starting at a very young age. Given what I have been learning lately about how insignificant our conscious thought processes are in comparison to our subconscious psychological machinery, these messages must take their toll.
What forms do gender biases take? There are many answers with varying applicability to me. Here is a non-comprehensive unordered list that I have assembled from reading things online, talking to people, and making my own observations.
- Intelligence and Competence Bias: This is simply the assumption that women are less intelligent or less competent than men. I have heard numerous stories in which Andrew launches into a lengthy explanation to Barbara about a subject in which Barbara is more of an expert than Andrew. Here is a particularly cringe-inducing example of this. I tend not to offer unsolicited explanations to men or women very often, and when providing solicited explanations I usually make an effort to identify my audience’s background, so I don’t think I am terribly guilty of this particular behavior. Instead, I notice this bias in myself when I am seeking an expert on a particular subject and I am presented with a male option and a female option. Sometimes I catch myself behaving or thinking according to the assumption that the male expert is more knowledgable or more adept than the female expert even if I have no particular reason to make such a judgement. I have to force myself to think deliberately about what I know and don’t know when making these sorts of comparisons.
- Experience Bias: Lately I have started noticing a disturbing pattern in my judgements about a person’s age, experience, education level, etc.: my estimates are consistently lower than reality for women and higher than reality for men. I have heard many stories from women in which they are demoted from faculty member to graduate student or from graduate student to undergraduate by a male interlocutor, and I am embarrassed to admit that I have done this before. I have also heard stories in which a female graduate student or faculty member has been assumed to be a secretary or staffperson; I don’t necessarily consider this to be a “demotion,” but I doubt I would appreciate it if it happend to me. These days I try to avoid guessing somebody’s position or experience level at all, and if I do make a guess it’s generally “faculty” regardless of gender (in a university setting). Still, it requires conscious effort on my part.
- Common Ground Bias: This is the assumption that, all things being equal, I will have more in common with a male than a female. This bias is fairly understandable – there are, after all, real biological and social differences between men and women – but I think it has unfortunate consequences in an academic setting. Few of my mathematical conversations with my peers begin, “Hi, my name is Paul. Would you like to have a conversation about elliptic cohomology?” Instead, they typically begin with the typical introductory social graces and lead into mathematical territory after a basic rapport has been established. This rapport is more difficult to establish with a person with whom I assume I will have a harder time identifying with before the conversation even begins, and consequently I am more likely to engage in mathematical conversations with my male colleagues than my female colleagues. I don’t know how socially isolated women in math departments feel, but I suspect that it’s more of a problem than I realize. My plan for reducing the impact of this bias is to simply be more bold and less awkward about engaging people in conversation, but this isn’t always easy.
- Sexual Biases: I am a heterosexual man who is attracted to intelligent and ambitious women, and the women that one finds employed in a math department often fit this description. Sexual attraction is firmly rooted in extremely powerful subconscious processes, and I am certain it affects my interactions with my female colleagues in ways that I don’t fully understand. If nothing else, it consumes some measure of my mental energy that is liberated when I’m interacting with men. It seems very hard to deal with the subconscious aspects of this bias, but I long ago adopted a mechanism which at least helps me manage the factors that are under my control. I decided early on in graduate school that I would categorically avoid romantically pursuing anyone in my own department. This allows me to sidestep the hazards associated with workplace romances in general, but mainly it helps me ensure that I treat all of my colleagues as professionally as possible. I don’t know how often the average woman in a math department is forced to deal with romantic overtures from her male colleagues, but given the highly skewed gender ratios I’m guessing it’s more than I imagine. I am also largely ignorant of the consequences of this behavior.
I’m sure there are other biases worth mentioning, but this list feels like a good start. One interesting supplementary observation about biases in general is that thinking about them leads to an unfortunate feedback loop: worrying about biases against women affects my behavior toward women. I think this effect is fairly minimal in comparison to the consequences of ignoring my biases and failing to monitor my behavior at all, but it’s there all the same.
My final remark about this subject is that there are many other bias issues which are also largely ignored by the mathematical and scientific community. I have encountered some discussion of racial bias in science, but I have heard almost no discussion about biases related to sexual orientation. If anyone reading this is aware of any studies or references about these issues, I would be interested in seeing them. Also, in this post I have focused on the effects of bias on my interactions with my colleagues, but the way my biases manifest themselves in my teaching is a whole other subject which I might take up in the future.
Feb 13, 2013 @ 21:14:12
Nice blog title (same name…) and thoughtful post. Here via geekfemisim.org.
Feb 13, 2013 @ 21:52:04
Thank you for your encouraging words on both accounts! I put a considerable amount of effort into both the blog name and the post.
Feb 27, 2013 @ 19:43:40
Thank you so much for this post, I’m bookmarking it for future reference.
Mar 20, 2013 @ 01:00:47
Applauses for your honesty. Great post.
Apr 24, 2013 @ 02:49:49
Thank you for your honesty – few people would have the courage to admit most of this to themselves, let alone in a blog.
As a female in the sciences, I’d be very interested in reading how you think your biases manifest themselves in your teaching. Having taken from a few grouchy old codgers myself, I often wonder if gender has any subconscious effect on grading.
May 17, 2013 @ 20:19:31
It’s final exam season, and I have been thinking very hard about biases in teaching and grading. Unfortunately, it’s very hard to tell and it may take time for me to further organize my thoughts. Here are some initial observations:
*I work pretty hard to avoid making snap judgements about any of my students (at least consciously), and consequently it’s hard to say anything about the role of my gender biases at the beginning of the semester when I don’t know anyone yet.
*My grading strategy is to lay out a detailed grading rubric before I start, and I try not to look at names. Of course, some things still come down to a judgement call and it is often possible to infer gender just from handwriting; I just don’t have enough data to say what happens in these situations. My guess is that the effect for me is fairly minor, but sometimes I daydream about ways I could do a controlled experiment.
*Common ground bias is an issue. When I learned the kind of math that I’m teaching these days, I would sort of haphazardly do lots of examples without providing my studying with much structure. I’ve caught myself assuming that my female students generally study in a more deliberate and organized way than my male students even though I encounter counter-examples on a regular basis.
*What I called “sexual bias” in my post is a very tricky issue in the context of teaching. Aside from the mental energy that is consumed by the part of my brain that is attracted to other people whether I like it or not, I also have to spend mental energy to regulate my behavior and ensure that I don’t express the attraction. One effect is that I sometimes overcompensate and act slightly more professionally around my female students than my male students. At this point I have enough experience that I think one would have to really scrutinize my behavior to observe this, but it’s probably still there.
Does any of that resonate with your experiences? I’ll undoubtedly keep thinking about all this, and if I come up with anything more concrete I may write another post
May 17, 2013 @ 16:10:11
“Bias is an inclination of temperament or outlook to present or hold a partial perspective at the expense of (possibly equally valid) alternatives in reference to objects, people, or groups.” –wikipedia
This is the definition of bias. The “biases” you have listed are not biases.
They do not represent a “partial perspective at the expense of equally valid alternatives”. To the contrary they represent a very fine approximation of reality. What we put into categories of typical male / female behaviours and aptitutes / interests is an accurate picture of reality as far as statistical averages are concerned. In other words: It is biology not socialization. Evidence for this can be found in intriguing studies by Simon Baron-Cohen who noted a correlation between testosterone level of the fetus and later interest in mechanical objects / vs. pictures of faces and people in only 2 days old babies. This shows that biology should not be ignored.
You cited no evidence at all, beside one anecdote and an over-emotional blog post by a histrionic female which both does not count for evidence.
You provided one piece of evidence: The evaluation of applicants. Why are women perceived as less capable than men? Is this a bias? No. Again the definition of bias is not met. It is an observation of a phenomenon that holds statistically true because of biological reasons. Men are generally / on average more capable and more interested in certain fields. Women who are interested in technical subjects also tend to have a higher level of testosterone during development. This is demonstrated by the studies of Barron-Cohen.
We as mathematicians should not get confused by emotions when evaluating the real world and up preferring to believe what “feels right” to what is actually true.
May 17, 2013 @ 19:33:33
I can’t help but notice that you provided a definition of the word bias but then dropped the word “possibly” when applying it. Setting aside the nature versus nurture debate for the moment, isn’t the mere possibility that I am underestimating the intelligence, experience, and friendliness of some of my colleagues reason enough to reflect on my behavior? In fact, these dangers are more than just possibilities: my gender-based judgement *actually* has led me astray in the past, sometimes with unfortunate consequences.
You seem to be arguing that instead of looking for patterns in our own behavior and trying to understand the consequences of those patterns, we should instead do statistical analysis on a given person’s secondary characteristics and calibrate our behavior according to the conclusions of that analysis. Frankly, this seems exhausting. Do I need to do statistics on race as well? Social class? Hair color? To me, it sounds much more straightforward to try to avoid making assumptions about things like intelligence and experience based on secondary characteristics and instead gather more direct information by, say, talking to people or looking at their accomplishments. This has the advantage of avoiding sampling error in statistical analysis. Of course, one must first be confident in one’s ability to evaluate characteristics like intelligence and experience objectively, and this is not always so easy.
Finally, I’m not sure what you mean when you wrote that I “cited no evidence”. As I wrote, the goal of my post was to analyze my own gender-oriented biases, and the evidence that I provided for the existence of those biases consisted of examples of ways in which I have been biased against women in the past. If you are still not convinced, I can probably provide more examples. It sounds like you were for some reason expecting me to provide evidence that gender differences in intelligence are social rather than biological. As I wrote in my post I’m not really interested in wading into this debate because I haven’t done my homework and because I don’t really see why it matters (as I tried to explain above). I will only add that the study referenced in Cathy O’Neil’s blog about world-wide gender differences in high school science test scores is more persuasive to me than a study about the impact of testosterone on toddlers.
To conclude what could have been a second blog post, we as mathematicians should recognize that we are human beings who have biases which distort our perceptions of others no matter how hard we try to be objective. (We should also apply definitions carefully when making arguments.)
May 18, 2013 @ 12:13:02
I don’t want to start a prolonged discussion but let’s grant for the moment that what you describe are actually “biases” (which I still maintain they are not).
These presumed biases are unconscious. So if they actually exist you cannot consciously change them. We are therefore talking about something which is certainly not scientific. Trying to consciously change a pattern of behaviour which doesn’t exist or only maybe exists probably leads to actual bias and actual discrimination.
It helps for example sometimes in math to get harsh criticism if you make mistakes or are simply wrong on something. One learns and grows from this, at least I certainly have. Maybe you are now hesistent to correct a female colleague in a direct and open way, thereby failing to help her grow the same way. Simply because of an imaginary bias you beat yourself up over.
And when it comes to studies it helps always to question the methodology, data and in this case the underlying assumptions.
There are also studies that show that students in school who are prettier get better grades. Females consistently do better in school than boys, at least in Germany and the UK. The study on science tests given to high-schooler’s in itself qualifies to hightlight that the curriculum is increasingly designed for female sensibilities. To now look for biases as an “explanation” for why the US is different, reveals mistaken assumptions.
It should never be the case that girls can “outperform” boys on science exams. Except actual discrimination is going on inside the school system, against boys.
Why do I say it should never be the case? This is not meant provocatively as it might sound.
I provide some more data for you on this issue: http://www.iqcomparisonsite.com/SexDifferences.aspx
May 18, 2013 @ 15:42:10
You bring up two interesting and important points which I think deserve further exploration. First, I agree that much (but not all) of what I have described is unconscious, and it is not clear how to change unconscious behavior using conscious thought. But it is definitely possible – there is a huge amount scientific literature on the subject! I’m not sure how it works in the context of interpresonal biases, but the first step is usually becoming consciously aware of the problem. Second, however, I agree that there are risks associated with become aware of the problem; this is the “feedback loop” I alluded to at the end of my post. But in my experience I think the effect has declined over time as I have become more aware of and comfortable with my own behavior. Regardless, I think it is better to be aware of a problem than not.
Regarding test scores, I certainly agree that the test score data don’t prove that the U.K. is doing a “better” job than the U.S. The real significance of the study is that it shows that the effect of any biological differences between males and females can be easily overwhelmed by socialization. Thus unless you believe that women in science in America never face negative discrimination based on their gender (a belief which in my opinion requires willfull ignorance), the study strongly suggests that social factors are negatively skewing girls’ science education outcomes. So again, the debate over biological differences between the genders is irrelevant. (Though surely you know that IQ scores aren’t part of that debate – if they were based on biology then the Flynn effect would not exist.)