Self-Identifying Factors include.. Diversity is often measured in open source communities through surveys by factors such as: ●Geographic region (i.e. city, state, country, continent) ●Role in an organization (i.e. developers, managers, executives) ●Types of problems someone works on (i.e. design, front-end engineering) ●Experience level (i.e. number of months or years working with a project) ●Project community (i.e. Gophers are members of the Go community) Diversity is also been measured in open source communities by factors such as: Gender, Age, Sexual orientation, Race, Ethnicity Known Measurement Problems ●Survey Design1 ●Biased sampling ●Oversimpliﬁcation ●Overstating results Representation and counting people matters, but for whom do these kinds of statistic serve? Who does this data belong to? We must also consistently ask of our demographic collection and goals: ●Who does this serve? ●Who does this leave out? ●Who does this harm? ●Who does this expose? Patterns and Anti-Patterns when Measuring Diversity in Open Source amanda casari — amcasari@google.com References 1. "Writing Survey Questions", Pew Research Center, https://www.pewresearch.org/our-meth ods/u-s-surveys/writing-survey-questio ns/ 2. Data Feminism, Catherine D'Ignazio and Lauren F. Klein, (2020) 3. Golang Developer Survey, Go Community (2021). 4. Blaise Agüera y Arcas, Margaret Mitchell and Alexander Todorov, Physiognomy’s New Clothes, (2017). 5. Lockhart, J.W., King, M.M. & Munsch, C. Name-based demographic inference and the unequal distribution of misrecognition. Nat Hum Behav (2023). Open Source Programs Office opensource.google About this Project Google Open Source's mission is to bring the value of open source to Google and all of the resources of Google to open source. We believe that open source solves real-work problems for everyone. Because when we say everyone, we mean everyone. So, how do we know we are - enabling everyone? encouraging everyone? creating space for everyone? So...how do you measure "everyone"? We set specific goals. We measure against them. We aim to do even better. Pattern: Understand Your Intent + Design with Purpose 1. Diversity, equity, and inclusion programs may be centralized or decentralized, but DEI work is integrated into all Google human-centered programs 2. If your mission statement includes words like "everyone", "all", or "community", you will want to know how your work is meeting those goals Anti-Pattern: Tacking demographic questions onto any available mechanism 1. Relying on demographic questions from ad-hoc surveys can complicate survey design 2. Regularly scheduled data collection and transparent analysis demonstrate commitment to community goals Pattern: Collecting Data for Accountability 1. Be clear + transparent with your communities on your diversity goals 2. Explain why you ask speciﬁc questions + what you ﬁnd in plain language a. "These questions allow us to measure diversity in the community and highlight opportunities for outreach and growth." (Golang Developer Survey 2021) Anti-Pattern: Every Single Inferential Method assigning identity At Google, we don't use, recommend, or release products which algorithmically infer gender, race, age, sexual orientation, ethnicity … These inferential algorithmic methods continue to be debunked as pseudoscience, even when dressed in technology: 1. Physiognomy’s New Clothes, 20174 2. Name-based demographic inference and the unequal distribution of misrecognition, 20235 Measuring Diversity "Measuring diversity" speciﬁcally refers here to measuring differences in self-identifying factors that people use to conﬁrm themselves in relation to others. Using principles from biodiversity + ecosystem diversity, we focus on more than just individuals or ﬂattened proﬁles. ●"Nature abhors a vacuum" or "Nature ...umm...ﬁnds a way." We are not measuring in a vacuum, designing for monocultures, or assuming spherical cows. Pattern: Ask Well-Designed Questions (build with!) 1. Designing