Unconscious biases are stereotypes about certain groups of people that individuals form outside their own conscious awareness. Nearly all our thoughts and actions are influenced, at least in part, by unconscious impulses. There’s no reason bias should be out of scope. Categorizing people based on social and other characteristics is a powerful survival mechanism, as it helps to distinguish friends from foes and make quick "life or death" decisions based on "inner feeling". At the same time this is a fertile ground for growing stereotypes, prejudice, discrimination.
Biases help the brain create shortcuts for the decision-making process and detect threats. Our unconscious biases are based on our own experiences and they help us detect patterns and find in-groups, a basic survival mechanism below our conscious radar. If unconscious bias goes unchecked, it can lead to fixed general views of how people should act or behave, and/or negative out-spoken attitude towards a person or group.
Everyone has unconscious biases, the goal is to bring them to our consciousness and navigate them. In order to provide a more inclusive and empathetic work environment.
Unconscious bias is far more prevalent than conscious prejudice and often incompatible with one’s conscious values. Therefore it would be good to have an instrument to detect and fight it. The tool that achieved most popularity both in scientific circles and public is the implicit-association test (IAT), which is a collaborative research effort between researchers at Harvard University, the University of Virginia, and University of Washington. It is meant to reveal the strength of one's mental association between certain groups of people and certain traits. It is used to investigate biases in racial groups, gender, sexuality, age, and religion, as well as assessing self-esteem.
Though it has enough criticism, IAT can jumpstart our thinking about hidden biases:
The SPACE2 Model of Inclusion - Six evidence based techniques for managing bias in oneself and others:
On 2020-06-24 we held three Live Learning sessions to cover how to recognize bias. This recording is from the second session and includes content as well as a Q&A portion. The content in the video below follows along with this slide deck and meeting agenda. We also used Mentimeter during the sessions to ask the attendees questions. GitLab team members can view the Mentimeter results.