# A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems

arXiv: https://arxiv.org/abs/2104.11600 · HTML: https://mlsanity.com/paper/2104.11600/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 75 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear Analysis](https://mlsanity.com/paper/2007.07129/) (2020)
- [Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration](https://mlsanity.com/paper/1801.09854/) (2018)
- [Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry](https://mlsanity.com/paper/2008.01053/) (2020)

## Built on by (most cited first, 2 of 2)

- [Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning](https://mlsanity.com/paper/2207.06835/) (2022)
- [Deep Learning Strategies for Industrial Surface Defect Detection Systems](https://mlsanity.com/paper/2109.11304/) (2021)

## Abstract (arXiv)

Computer vision (CV) techniques try to mimic human capabilities of visual perception to support labor-intensive and time-consuming tasks like the recognition and localization of critical objects. Nowadays, CV increasingly relies on artificial intelligence (AI) to automatically extract useful information from images that can be utilized for decision support and business process automation. However, the focus of extant research is often exclusively on technical aspects when designing AI-based CV systems while neglecting socio-technical facets, such as trust, control, and autonomy. For this purpose, we consider the design of such systems from a hybrid intelligence (HI) perspective and aim to derive prescriptive design knowledge for CV-based HI systems. We apply a reflective, practice-inspired design science approach and accumulate design knowledge from six comprehensive CV projects. As a result, we identify four design-related mechanisms (i.e., automation, signaling, modification, and collaboration) that inform our derived meta-requirements and design principles. This can serve as a basis for further socio-technical research on CV-based HI systems.