# Assessing Data Quality of Annotations with Krippendorff Alpha For Applications in Computer Vision

arXiv: https://arxiv.org/abs/1912.10107 · HTML: https://mlsanity.com/paper/1912.10107/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [A semantics-driven methodology for high-quality image annotation](https://mlsanity.com/paper/2307.14119/) (2023)
- [Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations](https://mlsanity.com/paper/2409.09412/) (2024)
- [What Makes a Good Generated Image? Investigating Human and Multimodal LLM Image Preference Alignment](https://mlsanity.com/paper/2509.12750/) (2025)
- [KαLOS finds Consensus: A Meta-Algorithm for Evaluating Inter-Annotator Agreement in Complex Vision Tasks](https://mlsanity.com/paper/2603.27197/) (2026)
- [Crowdsourcing of Real-world Image Annotation via Visual Properties](https://mlsanity.com/paper/2604.14449/) (2026)
- [Mixed-Modality Dual Face-Hair Retrieval](https://mlsanity.com/paper/2606.03470/) (2026)
- [Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment](https://mlsanity.com/paper/2608.14942/) (2026)

## Abstract (arXiv)

Current supervised deep learning frameworks rely on annotated data for modeling the underlying data distribution of a given task. In particular for computer vision algorithms powered by deep learning, the quality of annotated data is the most critical factor in achieving the desired algorithm performance. Data annotation is, typically, a manual process where the annotator follows guidelines and operates in a best-guess manner. Labeling criteria among annotators can show discrepancies in labeling results. This may impact the algorithm inference performance. Given the popularity and widespread use of deep learning among computer vision, more and more custom datasets are needed to train neural networks to tackle different kinds of tasks. Unfortunately, there is no full understanding of the factors that affect annotated data quality, and how it translates into algorithm performance. In this paper we studied this problem for object detection and recognition.We conducted several data annotation experiments to measure inter annotator agreement and consistency, as well as how the selection of ground truth impacts the perceived algorithm performance.We propose a methodology to monitor the quality of annotations during the labeling of images and how it can be used to measure performance. We also show that neglecting to monitor the annotation process can result in significant loss in algorithm precision. Through these experiments, we observe that knowledge of the labeling process, training data, and ground truth data used for algorithm evaluation are fundamental components to accurately assess trustworthiness of an AI system.