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

TL;DR

This paper explores empowering domain experts to directly develop ML systems through interactive teaching, aiming for more humane, inclusive, and potentially more intelligent AI by leveraging community collaboration and high-level knowledge sharing.

Contribution

It introduces the concept of expert-centric machine teaching, discusses technical challenges, and advocates for community-driven development to enhance inclusivity and system intelligence.

Findings

Expert-centric ML systems can be more humane and inclusive.

Community involvement can lead to more diverse and robust ML systems.

Technical challenges include designing interactive teaching interfaces and managing high-level knowledge.

Abstract

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.