Charting the ‘Data for Good’ Landscape
Jake Porway is a research fellow at data.org. He co-founded and served as Executive Director at DataKind, a non-profit dedicated to using data science in the service of humanity.
There is huge potential for data science and AI to play a productive role in advancing social impact. However, the field of “data for good” is not only overshadowed by the public conversations about the risks rampant data misuse can pose to civil society, it is also a fractured and disconnected space. There are a myriad of different interpretations of what it means to “use data for good” or “use AI for good”, which creates duplicate efforts, nonstrategic initiatives, and confusion about what a successfully data-driven social sector could look like. To add to that, funding is scarce for a field that requires expensive tools and skills to do well. These enduring challenges result in work being done at an activity and project level, but do not create a coherent set of building blocks to constitute a strong and healthy field that is capable of solving a new class of systems-level problems.
We are taking one tiny step forward in trying to make a more coherent Data for Good space with a landscape that makes clear what various Data for Good initiatives (and AI for Good initiatives) are trying to achieve, how they do it, and what makes them similar or different from one another. One of the major confusion points in talking about “Data for Good” is that it treats all efforts as similar by the mere fact that they use “data” and seek to do something “good”. This term is so broad as to be practically meaningless; as unhelpful as saying “Wood for Good”. We would laugh at a term as vague as “Wood for Good”, which would lump together activities as different as building houses to burning wood in cook stoves to making paper, combining architecture with carpentry, forestry with fuel. However, we are content to say “Data for Good”, and its related phrases “we need to use our data better” or “we need to be data-driven”, when data is arguably even more general than something like wood.
