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Updated August 24, 2022You’re reading an excerpt of The Holloway Guide to Technical Recruiting and Hiring, a book by Osman (Ozzie) Osman and over 45 other contributors. It is the most authoritative resource on growing software engineering teams effectively, written by and for hiring managers, recruiters, interviewers, and candidates. Purchase the book to support the author and the ad-free Holloway reading experience. You get instant digital access, over 800 links and references, commentary and future updates, and a high-quality PDF download.
The above statistics speak to a much deeper history of systemic bias and discrimination toward people from all these underrepresented groups.
Definition A bias is an inclination in favor of or against a particular person or group based on factors such as race, ethnicity, age, educational prestige, appearance, and so on. As a type of observational error, bias leads to unfair and ineffective outcomes when it affects decision-making. Bias can operate systematically where an individual’s decision-making is persistently affected or on a systemic basis where a process or group dynamic produces biased decisions. Explicit biases (or conscious biases) are inclinations about particular people or groups that an individual has and is aware of. Implicit biases (or unconscious biases) are inclinations based on subconscious associations that influence our decisions without us being aware of them.*
Biases are a kind of mental shortcut; rather than treating everyone as a complex individual, we pick factors by which to group them and assign that group certain expectations, with no evidence that those expectations are borne out in individual behavior. Implicit or unconscious biases are especially difficult to deal with because we don’t know that we have them, and they may even contradict the views we consciously hold. For example, a group of physicians may not consciously believe that Black patients feel less pain than white patients, but nevertheless recommend less pain medication for their Black patients experiencing the same injury as their white patients.*
There are many types of unconscious bias that affect people in general, and thus also affect hiring processes. Harvard developed a free online tool called the Implicit Association Test (IAT) to help people become more aware of their unconscious biases.
But there are plenty of conscious biases as well. For example, an overreliance on traditional academic pedigrees when screening for candidates vastly favors people who could afford a costly education and had access to resources and networks as a young person. We cover more biases throughout this section to help raise awareness and to provide practical suggestions on how to discuss and combat them in your organization.
Systemic bias and discrimination are a broad problem that affects not just recruiting and hiring, but also people’s willingness to remain in a given industry that does not represent them or treat them fairly. The Kapor Center’s landmark Tech Leavers Study reported in 2017 that nearly 40% of people who left the tech industry cited “unfairness or mistreatment” as the major reason they left, with men of color the most likely to leave due to mistreatment; 78% reported having experienced unfair treatment. In 2016, the departure rate for women was 41%—more than twice that of men, which was 17%.*
Underrepresented men and women of color experience stereotyping at twice the rate of their white and Asian peers, while LGBTQ+ tech leavers report bullying and public humiliation at significantly higher rates than other underrepresented groups. However, 62% of tech leavers said they would have stayed had their employer made efforts to create a more inclusive work environment.
Along with the negative consequences for candidates and employees, homogeneous and inequitable, unfair work environments also pose significant risks to organizations.* The Tech Leavers Study concluded that the industry stands to lose more than $16B per year in employee replacement costs.* Companies also may face backlash and negative brand associations for not dealing with potentially harmful features and unforeseen consequences of their products.*** For example, Facebook’s “real name” policy—which failed to realize the importance of privacy concerns of people from marginalized groups—was so controversial there’s an extensive Wikipedia page about it. When Twitter was in the running for acquisition, a number of potential buyers apparently balked at the company’s inability to deal with the harassment issues on its platform.