purpose, power, and pay
In this presentation, researchers from Metagov will present our findings from a study which involved the design and implementation of a tool called CollectiveVoice. The tool helps self-governing communities escape traditional admin-user models on digital platforms (specifically Open Collective and Slack) and enables them to experiment with and implement their own unique governance practices for decision-making – specifically about shared finances. As part of the study, we conducted interviews with 15 leaders of self-governing organizations to understand their challenges with community self-governance and what is needed to overcome those challenges both on social and technological levels. Then, using the insights from the interviews, we built CollectiveVoice, a no-code application version of PolicyKit which allows collectives to get more community members involved in the expense approval process. Once the tool was built, we designed a pilot program to support ~5 Open Collective communities in implementing a policy on CollectiveVoice. In this presentation, we will share the findings from our study to showcase if and how the CollectiveVoice tool impacted governance in the communities we worked with.
Val Elefante is a freelance researcher, project manager, strategic designer, and community manager specializing in community-led, responsible, public interest technology development. Currently, she is a Project Manager and Researcher at The Metagovernance Project working on a tool that helps communities with collective decision-making over shared finances called CollectiveVoice (powered by PolicyKit and Metagov Gateway). She is also the Head of Community at Reliabl, a feminist technology company building more equitable and participatory data classification and content moderation systems for community platforms, which have been successfully implemented on live applications including Lips (lips.social).
Nick Vincent is an assistant professor in Computing Science at Simon Fraser University. His research focuses on studying the dependence of modern computing technologies, such as AI systems, on human-generated data, with the goal of mitigating negative impacts of these technologies and working towards AI that mitigates inequalities in wealth and power rather than exacerbating them. Much of this work aims to make people aware of the value of their data contributions and help them leverage this value, and relates to concepts such as “data dignity”, “data as labor”, “data leverage”, and “data dividends”.