The results of any Machine Learning project are affected by a range of very human, not-so-autonomous decisions. Based on our experiences in both research projects and large-scale industry settings, Jenn Thom and I presented six types of those decisions at the AAAI Spring Symposium on the UX of ML. This includes your goals and the metrics you optimize for, implicit and explicit user feedback mechanisms and how you collect your training data. We also specifically call out a group that sometimes gets overlooked: the data editors and curators involved in selecting and annotating the data that machines learn from.
Read our position paper here, or check out the visual summary of my talk by the fabulous Chris Noessel:
— Chris Noessel (@chrisnoessel) March 27, 2017