# Human-AI Interaction Design in Machine Teaching

arXiv: https://arxiv.org/abs/2206.05182 · HTML: https://mlsanity.com/paper/2206.05182/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Can Neural Machine Translation be Improved with User Feedback?](https://mlsanity.com/paper/1804.05958/) (2018)
- [An Overview of Machine Teaching](https://mlsanity.com/paper/1801.05927/) (2018)
- [Iterative Machine Teaching](https://mlsanity.com/paper/1705.10470/) (2017)
- [Machine Teaching: A New Paradigm for Building Machine Learning Systems](https://mlsanity.com/paper/1707.06742/) (2017)
- [Exploring Human-like Attention Supervision in Visual Question Answering](https://mlsanity.com/paper/1709.06308/) (2017)
- [ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems](https://mlsanity.com/paper/1409.4814/) (2014)
- [A Framework for Interactive Knowledge-Aided Machine Teaching](https://mlsanity.com/paper/2204.10357/) (2022)
- [Interactive Semantic Featuring for Text Classification](https://mlsanity.com/paper/1606.07545/) (2016)

## Built on by (most cited first, 1 of 1)

- [Can Active Label Correction Improve LLM-based Modular AI Systems?](https://mlsanity.com/paper/2401.05467/) (2024)

## Abstract (arXiv)

Machine Teaching (MT) is an interactive process where a human and a machine interact with the goal of training a machine learning model (ML) for a specified task. The human teacher communicates their task expertise and the machine student gathers the required data and knowledge to produce an ML model. MT systems are developed to jointly minimize the time spent on teaching and the learner's error rate. The design of human-AI interaction in an MT system not only impacts the teaching efficiency, but also indirectly influences the ML performance by affecting the teaching quality. In this paper, we build upon our previous work where we proposed an MT framework with three components, viz., the teaching interface, the machine learner, and the knowledge base, and focus on the human-AI interaction design involved in realizing the teaching interface. We outline design decisions that need to be addressed in developing an MT system beginning from an ML task. The paper follows the Socratic method entailing a dialogue between a curious student and a wise teacher.