The cost of an error: Balancing the role of humans and machines

I had the opportunity to ponder this while taking cover in a bus shelter during a sudden Austin deluge. While weather forecasts driven by advanced modeling systems are quite useful, a part of me knows to always hedge against their inherent unreliability. In this sense it's not surprising that most of the early success of machine learning in the enterprise has clustered around low-error-cost problems. Models for targeting ads, or recommending products, friends or connections, do not wreak havoc when they misfire. Most end users of the system are not attending closely to the suggestions.