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This paper argues that a possible way to escape from the limitations ofcurrent machine learning (ML) systems is to allow their development directly bydomain experts without the mediation of ML experts. This could be accomplishedby making ML systems interactively teachable using concepts, definitions, andsimilar high level knowledge constructs. Pointing to the recent advances inmachine teaching technology, we list key technical challenges specific for suchexpert-centric ML systems, and suggest that they are more humane and possiblymore intelligent than traditional ML systems in many domains. We then arguethat ML systems could also benefit greatly from being built by a community ofexperts as much as open source software did, creating more inclusive systems,in terms of enabling different points-of-view about the same corpus ofknowledge. Advantages of the community approach over current ways to build MLsystems, as well as specific challenges this approach raises, are alsodiscussed in the paper.

Machine Teaching by Domain Experts: Towards More Humane,Inclusive, and Intelligent Machine Learning Systems