Title:AI Sees What? The Good, the Bad, and the Ugly of Machine Vision for Museum Collections
Authors:Brendan Ciecko
Publication:MW2020: MuseWeb 2020

Recently, as artificial intelligence (AI) has become more widespread and accessible, museums have begun to make use of this technology. One tool in particular, machine vision, has made a considerable splash in museums in recent years. Machine vision is the ability for computers to understand what they are seeing. Although the application of machine vision to museums is still in its early stages, the results show promise. In this session, we will explore the strengths and successes of this new technology, as well as the areas of concern and ethical dilemmas it produces as museums look towards machine vision as a move to effortless aid in the generation of metadata and descriptive text for their collections.

Over the course of several months, we have collected data on how machine vision perceives collection images. This study represents a sustained effort to analyze the performance and accuracy of various machine vision tools (such as Google Cloud Vision, Microsoft Cognitive Services, AWS Rekognition, etc.) at describing images in museum collection databases. In addition to thoroughly assessing the AI-generated outputs, we have shared the results with several prominent curators, and museum digital technology specialists, collecting expert commentary from such museum professionals on the fruits of this research.

Now, we strive to share our results. Our study represents over 100 hours worth of time invested in technical analysis, data collection, and interpretation, and we want to share this knowledge to advance the conversation in the museum field.

The goal of this paper is to spark a discussion around machine vision in museums and encourage the community to engage with ongoing ethical considerations related to this technology. While machine vision may unlock new potentials for the cultural sector, when it comes to analyzing culturally-sensitive artifacts, it is essential to scrutinize the ways that machine vision can perpetuate biases, conflate non-Western cultures, and generate confusion.