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.

翻译：分析大规模视觉媒体集合仍然是一项具有挑战性的任务，尤其是在涉及问题信息和人类受试者的混合方法研究中。在此类分析中使用人工智能工具存在固化并加剧偏见、以及面临难以承受的计算和成本限制的风险。因此，我们转而采用几何计算机图形学和视觉方法，结合人在回路中的定性分析，来分析来自问题信息宣传活动的大量图像。我们通过实施色彩量化来分析美墨边境的在线仇恨图像，并结合对色彩量化历史及肤色标尺的历史主义追溯，来说明我们如何从这些方法的种族主义起源中获取信息并对其进行改造性使用。为此，我们以案例研究为基础，阐述了动机，并强调了更多研究者在其自身工作中考虑改造性使用此类方法的优势与风险的必要性。