Computer Vision and Conflicting Values: Describing People with Automated Alt Text

TL;DR

This paper examines the ethical issues of using computer vision for automated alt text generation, comparing corporate policies and museum practices, and highlights the complex normative dilemmas involved.

Contribution

It provides an analytic framework contrasting corporate and museum approaches to alt text, revealing the ethical tensions in automated image description.

Findings

Facebook's policies on identity in alt text are cautious and selective.

Museum practices favor manual, context-aware descriptions of cultural artifacts.

Automated alt text raises complex ethical and normative dilemmas.

Abstract

Scholars have recently drawn attention to a range of controversial issues posed by the use of computer vision for automatically generating descriptions of people in images. Despite these concerns, automated image description has become an important tool to ensure equitable access to information for blind and low vision people. In this paper, we investigate the ethical dilemmas faced by companies that have adopted the use of computer vision for producing alt text: textual descriptions of images for blind and low vision people, We use Facebook's automatic alt text tool as our primary case study. First, we analyze the policies that Facebook has adopted with respect to identity categories, such as race, gender, age, etc., and the company's decisions about whether to present these terms in alt text. We then describe an alternative -- and manual -- approach practiced in the museum community, focusing on how museums determine what to include in alt text descriptions of cultural artifacts. We compare these policies, using notable points of contrast to develop an analytic framework that characterizes the particular apprehensions behind these policy choices. We conclude by considering two strategies that seem to sidestep some of these concerns, finding that there are no easy ways to avoid the normative dilemmas posed by the use of computer vision to automate alt text.