Bring this project to life
Now more than ever those working toward racial justice and equity need intentional support. While Paperspace is a technology company, we can still contribute to the invaluable work that Data Scientists and Machine Learning Engineers are doing to effect social change.
As a team, we started a Racial Equity initiative that has, so far, provided online coding workshops, tuition support, and partnerships with people of color and black-owned businesses.
As part of our initiative, we set out to find the most compelling, impactful, and interesting projects, that harness the power of Data, for Justice. Below, we share a curated a collection of the most exemplary companies, projects, and resources we’ve discovered so far:
Data for Black Lives — Founded and run by women of color, D4BL sees “Data as protest. Data as accountability. Data as collective action.” Through direct collaboration between activists, organizers, and mathematicians, they are a “movement (to) create concrete and measurable change in the lives of Black people.”
Campaign Zero — This platform makes the state of affairs regarding police violence really easy for anybody to understand, learn more about, and take action upon (e.g. a form to email your local representative).
California Police Scorecard — Interactive, well-designed data dashboard showing that grades and scores (currently only) California police departments based on their policing tactics, with particular attention to violence & accountability, and with plans to expand to all 50 states.
Center for Policing Equity — “How do you measure justice?” the Center asks. Gathering scientists, race & equality experts, “data virtuosos,” and community leaders, this organization strives to embody “the path that science can forge towards public safety, community trust, and racial equity.” Make sure to check out the Center’s Kaggle dataset and competition page that's updated frequently.
DataKind — Collaborative evening, weekend, or multi-month project-workshops provide social change organizations with a pro bono data science team to tackle their particular problems & needs. This empowers the organizations with Data Science talent and resources large, for-profit corporations benefit from, while also showing data scientists how they can use their skills for human good.
DrivenData — A company that enabling data scientists to compete for the best solutions to statistical problems/questions faced by activist organizations, localities, educational institutions, and other groups working to address human rights violations/opportunities.
HRDAG — (Human Rights Data Analytics Group) — First stop: Human Rights. This organization works tirelessly to "build scientifically defensible, evidence-based arguments that will result in outcomes of accountability." Every project and initiative is driven by "evidence-based arguments that will result in outcomes of accountability."
Bayes Impact — Bayes is working on open source web apps that give law enforcement agencies a low-friction interface to collect important data that is both standardized and robust. "Our first product is a web tool called URSUS, built with the CA Attorney General and Department of Justice to help all 800 law enforcement agencies in California collect and report use of force data. At no cost."
Living Cities — Collaborating with established organizations, such as Code for America and the National Neighborhood Indicators Partnership, LC provides grants, coaching, and knowledge-sharing in support of city-level data solutions addressing the needs of low-income people.
Elon University’s Poverty & Social Justice Research Guide — For Data Scientists and ML Experts, this invaluable resource from serves as a vast repository of datasets including demographics, society & lifestyle, health, and socioeconomics (note: a little bit of digging required).
Whether you are a Data Scientist, ML Engineer, Activist, or just a Human who feels the call to contribute, we hope you find inspiration, or even your next move, in the resources above. Also, if you have a suggestion for this list, we would be very grateful if you’d share it with us by writing to email@example.com.
Add speed and simplicity to your Machine Learning workflow today