Reference-Based Metrics Are Biased Against Blind and Low-Vision Users’ Image Description Preferences (2024)

Abstract: Image description generation models are sophisticated Vision-Language Models which promise to make visual content, such as images, non-visually accessible through linguistic descriptions. While these systems can benefit all, their primary motivation tends to lie in allowing blind and low-vision (BLV) users access to increasingly visual (online) discourse. Well-defined evaluation methods are crucial for steering model development into socially useful directions. In this work, we show that the most popular evaluation metrics (reference-based metrics) are biased against BLV users and therefore potentially stifle useful model development. Reference-based metrics assign quality scores based on the similarity to human-generated ground-truth descriptions and are widely accepted as neutrally representing the needs of all users. However, we find that these metrics are more strongly correlated with sighted participant ratings than BLV ratings, and we explore factors which appear to mediate this finding: description length, the image’s context of appearance, and the number of reference descriptions available. These findings suggest that there is a need for developing evaluation methods that are established based on specific downstream user groups, and they highlight the importance of reflecting on emerging biases against minorities in the development of general-purpose automatic metrics.

## introduction as the internet becomes increasingly visual, longstanding accessibility issues blind and low-vision (blv) users face remain largely unresolved @xcite . visionlanguage models have enabled the automation of image-to-text description generation, which can be used to generate alt-text descriptions; this could make visual content accessible to blv users through, e.g., screen readers @xcite . however, these systems often do not directly address the needs of blv users @xcite , which recent work has started to partially attribute to the evaluation methods used @xcite . originating from machine translation and summarization literature, reference-based metrics such as bleu @xcite , meteor (banerjee and lavie, 2005), rouge @xcite , cider @xcite , and spice @xcite are the most common method for evaluating descriptions. these metrics require humangenerated ground-truth descriptions (i.e., references) for scoring. with these, reference-based metrics quantify the similarity of the proposed description (i.e., hypothesis) to the ground-truth reference descriptions. the more similar the proposed hypothesis description is to the presumed "ideal" references (relevant content, sufficiently detailed, aligned with user preferences, etc.), the higher the assigned score for the description. to calculate this similarity, reference-based metrics make decisions on how to quantify semantic similarity, trade off the signal from multiple reference descriptions, and treat variation in description length. prior work suggests that blv users have strong preferences about description length @xcite and care greatly that they make sense in the context of where images appear @xcite . reference-based metrics do not necessarily make decisions on these variables, but their implications have not been explicitly evaluated for their potential downstream effects. with all of this in mind, we ask: when scoring descriptions, do reference-based metrics reflect the preferences of blv users? overall, we find converging evidence that metric design and common use actively favor sighted over blv user preferences, highlighting the need for automatic metric development that's grounded in downstream user needs. ## investigating sighted and blv user alignment reference-based metrics are intended to be neutral measures of similarity. since gold-standard reference descriptions are given, there is a priori no reason for the metrics to correspond more with the preferences of sighted vs. blv users. however, in practice, all metrics are based on implicit assumptions that might induce biases in their scoring. we test this explicitly by correlating assigned metric scores with sighted and blv participant judgments. while recent work suggests that image context shapes sighted and blv participants' preference ratings @xcite @xcite , these metrics tend to be used in context-agnostic settings. we therefore test these metrics in both context-sensitive and context-insensitive environments. ## understanding the misalignment to contextualize the misalignment with blv participant ratings, we analyze specific factors that might mediate the computed similarity. ## conclusion in contrast to the general perception of referencebased metrics as universally applicable, we find that they're significantly biased toward sighted and against blv user preferences. in an effort to understand this pattern, we find that these metrics do not correlate well with description length (which blv users care for greatly), and performance varies with regard to context and reference count for certain metrics more than others. our results highlight the necessity for developing reference-based metrics which put blv user needs at the center of their design and evaluation pipeline in order to mitigate the current bias.