Learn with these resources

Tools

Datasheets for Datasets, Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford – a framework for documentation as a tool for data equity

Data Nutrition Project – creating a standard label for interrogating datasets

Model Cards – a framework for transparency in machine learning models

Mitigating Bias in Artificial Intelligence Playbook, Berkeley Haas Center for Equity, Gender & Leadership – a playbook to help you understand why bias exists in AI systems and its impacts, beware of challenges to address bias, and execute 7 strategic ‘plays’

Feminist Data Manifest-No – a set of refusal-commitments toward fair data practices

Books & videos

Data Feminism, by Catherine D'Ignazio and Lauren F. Klein, The MIT Press, 2020 – approachable overview of equity issues in data

Design Justice, by Sasha Costanza-Chock, The MIT Press, 2020 – a framework for scoping data projects ethically

Invisible Women: Data Bias in a World Designed for Men, by Caroline Criado Perez, Chatto & Windus, Abrams, 2019 – great read on the intersection of gender and data

Data Feminism for AI: A Talk by Dr. Lauren Klein – data feminism principles applied to the current conversation about AI, its present harms, and its future possibilities

Organizations

Algorithmic Justice League – an organization that combines art and research to illuminate the social implications and harms of artificial intelligence

DAIR Institute – an interdisciplinary and globally distributed AI research institute rooted in the belief that AI is not inevitable, its harms are preventable, and when its production and deployment include diverse perspectives and deliberate processes it can be beneficial

We All Count – a project to increase equity in data science