Database Reference
In-Depth Information
DataKind works is by connecting volunteer data scientists with social orga-
nizations and the issues they're working on. We curate these collaborations
to make a big impact—bringing together teams of expert data scientists who
volunteer their time with social issue experts from mission-driven organiza-
tions—all focused on a data challenge. We think of our team as our staff and
volunteer leadership, but also a deep bench of expert data scientists and a
whole host of organizations, like Amnesty International, the World Bank, and
the United Nations.
I love my staff immensely—not only because they're incredibly brilliant people,
but also because I look up to them every day. They have such a flair for social
justice, for ethics, and for thinking about the real ramifications of what we do.
They're that perfect combination of technically-gifted people who are also
incredibly thoughtful, well spoken, and really want to make the world a better
place. We've been blessed that these great traits have also extended to the
volunteers, as well as the organizations who want to do good.
We think a lot about how to use data science to make a difference while
still remaining ethical. There's often a misperception that data science can be
scary. That it should be feared because people who use data science are just
using it blindly, using algorithms and things to manipulate our emotions on
Facebook or predict whether your daughter's pregnant based on what she
buys at Target. We are not like that and we all—the staff, the volunteers, and
the organizations we work with—strive very hard to make sure that we are
sensible, ethical, and really think about the real ramifications of what we do.
Gutierrez:
How does DataKind work?
Porway:
One of the ways that DataKind works is by connecting volunteer
data scientists with social organizations and issues, and then designing these
collaborations to make a big impact. One of the challenges in getting data
scientists to work on social problems is that data scientists themselves don't
always know the problem spaces. So if you're going to use data science to
alleviate hunger, for example, a data scientist might not necessarily know what
all the problems in hunger are. So we set up projects that are specifically
designed around collaboration with, say, a group of hunger alleviation experts
who've been doing this with the World Bank for fifty years, but don't have the
technical data science expertise. We unite those issue experts with expert
data scientists around a specific set of data challenges designed to make a big
impact for the beneficiary organization.
It would be great if we could do the matchmaking and just say, “You both now
know each other. Have fun.” The challenge on the other side is that the people
in the social sector side, who are new to data science, don't always know all
the ways it can be used. They may not know the right questions to ask. On the
flip side, the beneficiary organizations have the deep expertise around their
