Author’s note: This is a pre-publication draft. Parts of this material may appear in subsequent publications. I’m sharing in this format to enable transparency and dialogue but I do not wish to misrepresent the relationship this draft may have with any later work.
There’s a lot of talk of diversity, especially discussion of the reasons companies and other groups should prioritize diversity.
Diversity for fairness
One important idea is a general sense of fairness. Careers that pay well should be distributed roughly proportionately among genders, ethnic groups, and other ways of categorizing people. We have the sense that there’s nothing about being male that makes someone a better programmer, or about being white that makes someone a better hedge fund manager. Then fairness suggests to many that, any unevenness in who gets access to these desirable careers reflects a lack of fairness. When a tech company primarily is located in a country where black people make up 12% of the population, but that company’s tech workers are only 1% black (as is true of Alphabet/Google, Amazon, and other tech companies) then something unfair is going on. Sure, there is some buck-passing here. There are problems at many levels that create unfairness: geographic segregation, school systems, gaps in generational wealth accumulation, gaps in laws. But the culture of many of these tech companies is one that famously fights (and wins!) against structural hurdles like these. If these companies can move fast and break things when it comes to new features and their bottom line, we should expect the same when it comes to fairness.
Diversity for social tech
There are other reasons companies prioritize diversity, like legal ones, that I won’t get into. Instead I want to focus on something I would like to hear more often and more clearly from the technology world. Independent of everything else, diversity will become essential to the success of any tech company. This is because the products made by tech companies are increasingly cultural ones, embedded in our everyday social lives, and producing social products that are successful requires engineers/designers/etc. who have a social understanding of those who use the products.
Let’s not forget when Google Maps pronounced Malcolm X Street “Malcolm the Tenth” as if he were a british monarch[Baratunde Thurston]; that Facebook’s name policy locks the accounts of trans and gender non-conforming users, including a Facebook employee[Zoe Cat]; when Google Images automatically labeled black people gorillas[Jacky AlcinÃ©]; the long history of racial and gender bias in face recognition[Joy Buolamwini: Gender Shades]; the many cases of technology ignoring dark skin [like soap dispensers and heart rate monitors]. These are just a few examples but they illustrate the point that we are interacting with our technology in social ways. Each of the above examples would have been trivial to catch for an engineer, project manager, or other employee from an underrepresented group.
What I’m arguing here is that it’s in tech company’s self-interest to seriously prioritize diversity. Even a company that is unconvinced that diversity is important because of fairness or legal reasons should be concerned about how they plan to design products that people interact with in social contexts. These companies will want to ensure that their tech workforce is diverse at every levelâfrom the people writing the code to those deciding which new features to develop and beyondâbecause each of these decisions will become more significantly social as time goes on.
Diversity to stop algorithmic violence
One specific concern involves the idea of algorithmic violence[Mimi Onuoha], a term coined by Mimi Onuoha that refers to the ways that automated decision-making does real harm to people. In college I studied Computer Engineering and took an ethics course along with civil and mechanical engineers where we discussed the ethical challenges involved in designing walkways and other physical things. What was missing then, and seems to largely be missing now, is a serious look at how decisions made in technology companies, including by software developers and data scientists, lead to real consequences.
Some algorithmic violence is quite easy to see, such as when Palantir (one of the most valuable data companies and one that was recently sued for racial discrimination[Vanity Fair]) builds an enormous data machine for the targeting and tracking of immigrants for deportation[The Intercept]. In most cases, though, algorithmic violence is less than obvious. Guillaume Chaslot writes[Medium] that YouTube’s massive recommendation engine, one that he helped design, is tasked with maximizing users’ viewing time. The recommendation engine does this in a single-minded way that ignores the effects that the kind of content one cannot turn away from (like disturbing videos targeted at kids[Medium] and conspiracy theories[Vanity Fair]) might have on its viewers and even elections[Chaslot @ Medium]. In ‘Automating Inequality’[Strand] Virginia Eubanks provides an extensive catalogue of ways that seemingly âobjective’ automated systems harm vulnerable people, whether they were set up to do so intentionally or not.
Algorithmic violence is a problem, like information security and global warming, that is virtually guaranteed to become more important with time. Because the effects of algorithmic violence are often hidden except to those who are affected by it, companies without a diverse workforce will be at a disadvantage when trying to recognize and prevent such violence. So prioritizing diversity is one of the many steps, like a Hippocratic oath for technology[Marie], we need to take to counter algorithmic violence.
- Weapons of Math Destruction by Cathy O’Neil - [Strand link to purchase] (http://www.strandbooks.com/political-science/weapons-of-math-destruction-how-big-data-increases-inequality-and-threatens-democracy-0553418831/)
- Automating Inequality by Virginia Eubanks - [Strand link to purchase] (http://www.strandbooks.com/sociology/automating-inequality-how-high-tech-tools-profile-police-and-punish-the-poor/)
- On Algorithmic Violence by Mimi Onuoha - [github link] (https://github.com/MimiOnuoha/On-Algorithmic-Violence)
- Gender Shades, research project led by Joy Buolamwini - 2018 paper by Buolamwini and Gebru