# Crowdsourcing the Perception of Machine Teaching

arXiv: https://arxiv.org/abs/2002.01618 · HTML: https://mlsanity.com/paper/2002.01618/

## Key facts

- Cited by 9 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps](https://mlsanity.com/paper/2205.06935/) (2022)
- [Blind Users Accessing Their Training Images in Teachable Object Recognizers](https://mlsanity.com/paper/2208.07968/) (2022)
- [MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech](https://mlsanity.com/paper/2204.00145/) (2022)
- [Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challenges](https://mlsanity.com/paper/2408.03303/) (2024)
- [Exploring Machine Teaching with Children](https://mlsanity.com/paper/2109.11434/) (2021)
- [Collaborative Machine Learning Model Building with Families Using Co-ML](https://mlsanity.com/paper/2304.05444/) (2023)
- ["Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications](https://mlsanity.com/paper/2503.03927/) (2025)
- [Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations](https://mlsanity.com/paper/2509.25492/) (2025)
- [AccessShare: Co-designing Data Access and Sharing with Blind People](https://mlsanity.com/paper/2407.19351/) (2024)

## Abstract (arXiv)

Teachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users may conceptualize, experience, and reflect on their engagement in machine teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk. Using a performance-based payment scheme, Mechanical Turkers (N = 100) are called to train, test, and re-train a robust recognition model in real-time with a few snapshots taken in their environment. We find that participants incorporate diversity in their examples drawing from parallels to how humans recognize objects independent of size, viewpoint, location, and illumination. Many of their misconceptions relate to consistency and model capabilities for reasoning. With limited variation and edge cases in testing, the majority of them do not change strategies on a second training attempt.