Machine Teaching by Domain Experts: Towards More Humane,Inclusive, and Intelligent Machine Learning Systems
This work addresses the problem of making ML systems more accessible and collaborative for domain experts, though it is conceptual and incremental in nature.
The paper argues for developing machine learning systems that can be directly taught by domain experts using high-level concepts, aiming to create more humane and inclusive systems. It suggests that community-driven development could enhance these systems by incorporating diverse viewpoints.
This paper argues that a possible way to escape from the limitations of current machine learning (ML) systems is to allow their development directly by domain experts without the mediation of ML experts. This could be accomplished by making ML systems interactively teachable using concepts, definitions, and similar high level knowledge constructs. Pointing to the recent advances in machine teaching technology, we list key technical challenges specific for such expert-centric ML systems, and suggest that they are more humane and possibly more intelligent than traditional ML systems in many domains. We then argue that ML systems could also benefit greatly from being built by a community of experts as much as open source software did, creating more inclusive systems, in terms of enabling different points-of-view about the same corpus of knowledge. Advantages of the community approach over current ways to build ML systems, as well as specific challenges this approach raises, are also discussed in the paper.