Why “data for good” lacks precision. | by Sara Hooker | Towards Data Science

I just returned from a fantastic week in Stockholm attending International Conference on Machine Learning (ICML) 2018. One of the most active informal communities at ICML was the “data for good” community. We organized a few spontaneous lunches where I met some incredible researchers and applied practitioners. However, our discussions as a group made me revisit a gut reaction I have had for awhile that “data for good” has become an arbitrary term to the detriment of the goals of the movement. “Data for good” says little about the tools being used, the goals of the endeavor, or who we are serving. It is similar to the frequent use of “AI” to describe everything vaguely related to machine learning. Both are terms that are exciting in their use and general appeal but lack precision from the perspective of technical practitioners. I accept that “data for good” is a useful shortcut for talking to a broad audience (I also use it when talking to a general audience, as here in my twitter bio).

I just returned from a fantastic week in Stockholm attending International Conference on Machine Learning (ICML) 2018. One of the most active informal communities at ICML was the “data for good” community. We organized a few spontaneous lunches where I met some incredible researchers and applied practitioners. However, our discussions as a group made me revisit a gut reaction I have had for awhile that “data for good” has become an arbitrary term to the detriment of the goals of the movement. “Data for good” says little about the tools being used, the goals of the endeavor, or who we are servi