Data for Good Exchange:
@BloombergTech & @Data4Democracy's #DG4X.
(San Francisco, CALIFORNIA)
Worked with Natalie Evans Harris (fmr Senior Policy Advisor to US CTO), @BloombergTech and @Data4Democracy to form a community-driven approach for developing a code of ethics, or 'Hippocratic oath', for data scientists. Curated responses from 2,000 members of the global data science community and identified recurring themes that community members highlighted, arranging them into a systematic framework. Tasked working groups to distill the 75 drafted ethics principles into 6 core tenets, soliciting real time feedback from virtual participants via livestream, workshops, social media and Slack. Notable attendees included DJ Patil (fmr Chief Data Scientist of the United States), Doug Cutting, (co-creator of Hadoop), Joy Bonaguro (Chief Data Officer of San Francisco) and Gideon Mann.
WIRED; SHOULD DATA SCIENTISTS ADHERE TO A HIPPOCRATIC OATH?
In San Francisco, dozens of data scientists from tech companies, governments, and nonprofits gathered to start drafting an ethics code for their profession. The general feeling at the gathering was that it’s about time that the people whose powers of statistical analysis target ads, advise on criminal sentencing, and accidentally enable Russian disinformation campaigns woke up to their power, and used it for the greater good.
dJ PATIL (chief data scientist, US UNDER president obama); A Code of Ethics for Data Science
2.5 quintillion bytes of data are created every day. With the old adage that with great power comes great responsibility, it’s time for the data science community to take a leadership role in defining right from wrong. Much like the Hippocratic Oath defines Do No Harm for the medical profession, the data science community must have a set of principles to guide and hold each other accountable as data science professionals. To collectively understand the difference between helpful and harmful. To guide and push each other in putting responsible behaviors into practice. And to help empower the masses rather than to disenfranchise them. Data is such an incredible lever arm for change, we need to make sure that the change that is coming, is the one we all want to see.
DATA for democracy: code of ethics project
Data for Democracy is partnering with Bloomberg and BrightHive to develop a code of ethics for data scientists. This code will aim to define values and priorities for overall ethical behavior, in order to guide a data scientist in being a thoughtful, responsible agent of change. The code of ethics is being developed through a community-driven approach.By hosting discussions among data scientists, we hope to better capture the diverse interests, needs, and concerns that are at play in the community, and put together a code that is truly created by data scientists, for data scientists.
bloomberg: It’s Time for Data Ethics Conversations at Your Dinner Table
An idea that's gained traction is the need for a ‘Hippocratic Oath’ for data scientists. Bloomberg’s partnered with Data for Democracy and BrightHive, bringing the data science community together at Data for Good Exchange (D4GX) events to draft a set of guiding principles that could be adopted as a code of ethics. Notably, this is an ongoing and iterative process that must be community-driven, respecting and recognizing the value of diverse thoughts and experiences. The group re-convened on February 6th, 2018 at the inaugural D4GX event in San Francisco, again open to the public. Notable attendees included DJ Patil, who served as the Chief Data Scientist of the United States from 2015-2017, Doug Cutting, co-creator of Hadoop and advocate for open source, as well as representation from the National Science Foundation-funded Regional Big Data Innovation Hubs and Spokes program. At this event, participants reviewed over 75 drafted ethics principles formulated by several working groups, with the goal of distilling a larger group of tenets into a streamlined set of principles for an ethics code.
domino data lab: Community Principles on Ethical Data Sharing
Community Principles on Ethical Data Sharing (CPEDS): a crowdsourced effort to institute a code of ethics for data sharing across the data science community. Bloomberg hosted D4GX where dozens of us gathered in person and hundreds more tuned into the livestream programming to hear from inspiring speakers such as Gideon Mann, DJ Patil, Natalie Evans Harris, Joy Bonaguro, and Paula Goldman. The highlight was sharing the first draft of CPEDS with the community and soliciting feedback in real time via in-person workshops and Slack chats with the remote audience. The first set of principles addresses six topics: thought diversity, bias, privacy & security, responsible communications, provenance & ownership and transparency & openness.
FORBES: Bloomberg's Data Initiative: Big Data For Social Good In 2018
The Bloomberg Data for Good Exchange was launched in 2015 to encourage and promote the use of data science and human capital to solve problems at the core of society. Each year, the program focuses on themes pertaining to how data science can play a role in helping drive change in the delivery of public services, city operations, public health, climate resilience and the environment, criminal justice and other areas of public concern.
tech@BLOOMBERG: Q&A about data science code of ethics with Lilian Huang of Data for Democracy
We’ve recently completed a preliminary scan based largely in the Data for Democracy community – as mentioned, this is a group of over 2,000 people connected on Slack and Twitter. We posed discussion questions through those channels and community members responded. From these responses, we identified recurring themes that community members consider important, along with some concrete examples, and arranged them in a systematic framework. The key areas of concern we identified are: issues regarding data itself, issues regarding the questions and problems to work on, issues regarding the algorithms and models being used, issues regarding the technological products and applications that are created from research, and issues regarding the data science community and culture.