Q&A About Data Science Code of Ethics With Lilian Huang of Data for Democracy

Oct 3, 2017 2:35 PM ET

Originally posted on www.bloomberg.com.

A new initiative from Bloomberg, BrightHive, and Data for Democracy is looking to create shared values on the ethics of data science. In September, at Bloomberg’s 2017 Data for Good Exchange (D4GX), the group announced a project called the “Community Principles on Ethical Data Sharing” (or CPEDS), which is designed to develop a set of guidelines for data scientists related to data sharing and collaboration on data-driven projects. Tech@Bloomberg sat down with Lilian Huang, Data Ethics Lead at Data for Democracy, to get more detail on this important initiative.

What is Data for Democracy and why is it trying to create a data science code of ethics?
Data for Democracy is a grassroots technology collective with more than 2,000 volunteers from across the globe. We started out as an online community in December 2016, and now have multiple local chapters based in major cities. Our volunteers are data scientists, technologists and activists who partner with civic organizations and carry out open-source research, analysis and software development. Our goal is to explore and enhance the relationship between tech, government and society. 

Helping to create a data science code of ethics fits naturally into our mission, because it provides an opportunity to consider and articulate how data scientists can produce work that benefits society.

Why does the industry need a data science code of ethics?
Data science isn’t so much a single industry as it is an approach or methodological framework that’s applied across multiple domains and industries – policy, finance, medicine, marketing, and many more. As the data science “approach” becomes increasingly influential in more sectors, there’s also a heavy emphasis on collecting and storing data, and applying it for commercial and non-profit purposes. This brings up a lot of thorny issues, such as the safe storage of sensitive personal data, or the development of biased algorithms to target or exclude individuals.  As these ethical questions grow more pressing, it makes sense to outline a set of baseline responsibilities and ethical obligations that data scientists should consent to upholding – or at least, some values and priorities that they can consider – when trying to maximize the benefit and minimize the harm from their work.

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