Working with Color: How Color Quantization Can Aid Researchers of Problematic Information

TL;DR

This paper explores how color quantization, a technique from computer graphics, can assist researchers in analyzing problematic visual media, balancing AI tools with human qualitative analysis to address biases and limitations.

Contribution

It introduces a novel application of color quantization in analyzing problematic images, combining technical methods with critical historical context and human-in-the-loop analysis.

Findings

Effective use of color quantization in hate image analysis

Historical insights into color quantization and skin tone scales

Guidelines for reclaiming methodologies from racist origins

Abstract

Analyzing large sets of visual media remains a challenging task, particularly in mixed-method studies dealing with problematic information and human subjects. Using AI tools in such analyses risks reifying and exacerbating biases, as well as untenable computational and cost limitations. As such, we turn to adopting geometric computer graphics and vision methods towards analyzing a large set of images from a problematic information campaign, in conjunction with human-in-the-loop qualitative analysis. We illustrate an effective case of this approach with the implementation of color quantization towards analyzing online hate image at the US-Mexico border, along with a historicist trace of the history of color quantization and skin tone scales, to inform our usage and reclamation of these methodologies from their racist origins. To that end, we scaffold motivations and the need for more researchers to consider the advantages and risks of reclaiming such methodologies in their own work, situated in our case study.