How Implicit Bias Affects Diversity and Inclusion in Open Source

The importance of diversity and inclusion in open source communities and the need to address biases among individuals to achieve it.

• Minor issues can lead to exclusion of individuals in open source communities
• Modernizing the way communities welcome and get people to participate is necessary
• Data should be used to inform decisions on inclusivity
• Avoid generalization and be aware of differences among contributors
• Empathy and self-awareness are important in communication
• Diversity and inclusion bring benefits beyond productivity
• Group effort is needed to achieve diversity and inclusion in open source communities

The speaker shared a personal experience of being called out for bias and taking accountability by reaching out to the individuals in question. This highlights the importance of being self-aware and addressing biases among individuals to achieve diversity and inclusion in open source communities.

Open Source communities operate on a set of values that include a commitment to inclusion and diversity. The idea that the tech ecosystem at large is faced with the challenges of diversity and inclusion is not a myth. To some degree, everyone has implicit biases and beliefs about various social and identity groups. Implicit biases are unconscious attitudes or stereotypes that affect how we think, act, and make decisions. Studies have shown that implicit bias is the main cause of most of the problems caused by diversity and inclusion. As users, maintainers, and contributors of Open Source software, we may have encountered different marginalised groups that have shared their encounters of bias in Open Source communities. Majority of people are unaware of this bias and how it affects them as individuals or even as the victims. I'll go over what diversity and inclusion are in this session and why they're important in open source. Challenges faced by underrepresented groups in open source communities as a result of bias. What implicit bias is and how to spot it. Highlights of different forms of implicit bias, how to be more aware of this biases as a community and how it can be improved.