Why do we need a diverse workforce?

The Value of Multiple Perspectives

Basing decisions on a variety of perspectives takes a broader range of potential experiences into account, which is important no matter what you are trying to create. Looking at something from more angles allows you to see the thing more completely. Having a more complete view of a problem supports more effective problem solving and creates greater innovation.

In this context, assimilation refers to the process by which one group takes on cultural and other traits of a larger group, frequently under indirect social pressure or direct pressure from authority figures. In the workplace, this can show up as expectations around what types of clothes people wear, how they style their hair, what is considered a "professional" use of language, etc. In a classroom, while it can often show up in ways related to personal appearance, it can also show up as an expectation that all "successful" students will approach the work in the same way. If we make a point of getting people with a wide variety of identities and life experiences into a classroom, then expect that their path to success will require everyone to think about problem solving in the same way, we simultaneously:

• create obstacles for those who have not yet been trained in that particular way of thinking

• lose the greatest value of gathering a diverse group of people: the greater range of creative problem solving

Who decides which problems need solving?

Even before anyone starts considering solutions, it's important to consider which problems are getting solved and who decided they were worth solving in the first place. When the people who make these decisions represent a limited range of identities and experiences, whether they distribute grant funding, oversee industry R&D, or green-light movies, they are probably overlooking a lot of questions that deserve answers.

There are many examples of technology being developed in ways that ignored the needs, or even the existence, of whole portions of the user population.

One of the most infamous examples is facial recognition. Once the technology became more widely available, people started noticing that it had a harder time identifying certain kinds of faces, particularly those of women and darker skinned people. Aside from the emotional impact of being unrecognizable to a tool intended to recognize people, this flaw in the technology has huge implications for law enforcement and other forms of surveillance with the potential to cause very direct harm to people who are misidentified. (Want to learn more? Watch Coded Bias)

In Industry and the Academy

Both the tech industry and academia have common practices which limit the number of perspectives allowed into the larger conversation. For example...

In industry, technical interviews for entry level jobs create situations where the skills tested are not necessarily the skills most important for actually doing the job, and those who can pay for interview prep resources have a significant advantage. There are many people with skills and knowledge that would be useful to tech companies who simply don't perform well under the very specific conditions of technical interviews.

In academia, requiring "peer review" for someone's scholarship published and acknowledged as valid opens the door for all kinds of exclusionary bias. While it's important that academic scholarship have some form of accountability and quality control process, it's also important to ask the questions of "Who are the peers doing the reviewing, and what personal biases do they hold?" and "Who created the standards by which scholarship is judged?"