Bloomberg, BrightHive and Data for Democracy Launch Initiative to Develop Data Science Code of Ethics
When data scientists are entrusted with the most private and valuable data out there, the data science community must work to deserve the trust of those whose data we are holding.

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“Community Principles on Ethical Data Sharing” to provide guidelines for ethical behavior”

New York — At Bloomberg’s 2017 Data for Good Exchange, the fourth annual conference exploring how data science can help solve problems for social good, a partnership was announced between Bloomberg, BrightHive and Data for Democracy to develop a code of ethics for data scientists from the ground up. Called the “Community Principles on Ethical Data Sharing (CPEDS),” this code of ethics will provide a set of guidelines about responsible data sharing and collaboration.

“When data scientists are entrusted with the most private and valuable data out there, the data science community must work to deserve the trust of those whose data we are holding,” said Gideon Mann, Bloomberg’s head of data science.

Over the next six to nine months, the goal of the partnership is to define values and priorities for overall ethical behavior by data scientists. By developing a code of conduct around data sharing, data scientists can be thoughtful, responsible, and ethical agents of change in their organizations. Through social media and community discussions, more than 2,000 data scientists have already weighed in on what the challenges are with sharing data and what must be overcome for people and companies to feel comfortable sharing data with a trusted community of data scientists.

“We set out to explore what it could look like if data scientists had a code of ethics, similar to physicians’ Hippocratic Oath,” said Natalie Evans Harris, ‎COO and Vice President of Ecosystem Development at BrightHive, and a former senior policy advisor to the U.S. Chief Technology Officer under President Barack Obama. “The CPEDS won’t propose comprehensive solutions for thorny questions like how to minimize algorithmic bias. Instead, it will aim to define priorities for overall ethical behavior related to data sharing.”

The guidelines will be general enough to be relevant to a data scientist’s approach to any work or projects, while also being specific enough to support data scientists when they need to make difficult ethical decisions about their work.

“In other words,” said Lilian Huang, Data Ethics Lead at Data for Democracy, “they are meant to guide a data scientist in being a thoughtful, responsible, and ethical agent, who can then work together productively with other data scientists to actually solve problems such as how to minimize algorithmic bias or maintain data privacy.”

The preliminary work to date has been focused on framing a larger discussion and bringing together the community to move the conversation forward. This includes identifying recurring themes that members of the data science community consider important and arranging them in a systematic framework to address the following five areas of concern:

  • Data itself: Overall practices surrounding the collection, storage, and distribution of data and understanding and minimizing intrinsic bias in collected data;
  • Questions and problems: Identifying valuable and relevant problems to work on and working with pre-existing resources and parties in those fields;
  • Algorithms and models: Understanding and minimizing bias in algorithms/models and responsibly dealing with black-box algorithms;
  • Technological products and applications: Responsibility for how one’s research is applied; identifying and guarding against the potential for misuse;
  • Community: Fostering a data science community culture that is actively welcoming to people from diverse backgrounds and deliberately promoting equity and representation, and finding ethical, non-invasive ways to track progress.

Ultimately, the hope is to have over 100,000 data scientists participate in the community-driven process for shaping the CPEDS via social media, conversations and virtual working groups to share and collect best practices, techniques, and tools.

To participate in any of the conversations, individual data scientists can respond to discussion questions on TwitterSlack, or GitHub. Organizations interested in having a representative participate in bi-weekly focus group calls should contact [email protected]. Ongoing discussions will take place throughout October and November, with the findings consolidated and presented at the San Francisco Data for Good Exchange on December 7, 2017 at Bloomberg’s Engineering Hub, 140 New Montgomery Street. More details about this event will be announced shortly.

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BrightHive is a for-purpose data technology company using data trusts to transform the way social services providers share data, make decisions, and affect the behavioral outcomes of beneficiaries. Together with our strategic partners, we build and support the technical infrastructure necessary for national government, state and city governments, and social and civic organizations to share data more effectively and securely, and build interventions that impact the individuals’ life directly. For more information, reach out to Natalie Evans Harris ([email protected]) or Matt Gee ([email protected]).

Data for Democracy is a global, grassroots technology collective of over 2,000 volunteer data scientists, technologists, and activists strengthening democracy through partnerships, policy initiatives, and research. Through our online community and city chapters we support civic and mission-driven organizations, develop open source software, conduct analyses, and explore the relationship between tech, government, and society. For more information, reach out to Jonathon Morgan ([email protected]).