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The Framework for Responsible Data Science (RDS) is a dynamic, living document created by and for the data science community. It’s an approachable tool that guides organizations and data professionals in applying the four RDS principles—Fairness, Transparency, Privacy, and Veracity—to their data science projects. Our framework maps out key considerations and steps to find the most ethical outcomes possible.
We use the framework in our own projects and have made it readily available to others. We envision a world where RDS is the standard across all industries. While the second version was published in early 2024 (thanks to the work of our working group members and volunteers), we always strive to keep the framework relevant to the ever-evolving tech landscape.
Disclaimer about contributors: The RDS Framework is a product of collaboration by luminaries and professionals from various industries. Inclusion as a “Contributor” recognizes one’s participation in the document but does not imply that individual’s endorsement of all sections of the framework.
Kindly make checks payable to 501c(3): Data Science Alliance and use “RDS Framework Project” on any references or memos. Our mailing address is:
Data Science Alliance
2675 Rosecrans St.,
San Diego, CA 92106
When granting through your donor-advised fund, please use the following information:
Our legal name: Data Science Alliance
Tax ID/EIN: 84-3819214
Mailing address: 2675 Rosecrans St., San Diego, CA 92106
If you’d like to give a gift via wire or stock, please reach out to info@datasciencealliance.org for our account information.
We're able to accept donations in the form of stock, government (including municipal) debt, and corporate debt through the Depository Trust Company (DTC). For security, settlement, and reduced paperwork, electronic transfer through the DTC is preferred. This can be arranged from most brokerage accounts.
Our volunteers play a key role in developing future versions of the RDS Framework, providing insight and expertise on how it can be further refined and applied.
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