Without human purpose, a computer is just a rock that we tricked into thinking.
Evaluating the impact of ML models is a hot topic today, but this is the first writing I've seen that incorporates human outcomes into the process of algorithm design. This post by Data Science Roundup subscriber Chris Butler does just that: it reframes the construction of ML systems as "empathy maps", and asks what the algorithm needs to do, sense, say, think, and feel.
It seems like we're about as good at designing algorithms today as software developers in the 70's were at building mainframe systems. While technology has certainly improved in the ensuing years, so too has the way we have thought about constructing such systems.
While I don't know whether this particular approach is specifically the answer, I anticipate much more design thinking applied to algorithms.Read more...