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

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 highlevel 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.

How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study

“…This generates a situation of the Game of Telephone -i.e., additional mediators have to be involved to incorporate their domain knowledge [20,43]. We envision that with the right tool and education, we can take it to a more efficient way of conversation and avoid the game of telephone.…”

Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models

“…The HCI community is further concerned with the isolation of domain experts from the model development process, requiring thus data scientists to go through lengthy and asynchronous iterations to get their input [9,60]. To tackle the problem, the sub-field of interactive machine learning (iML) is motivated to enable domain experts or end-users to directly drive model behaviors [9,38,83].…”