The Case for "Thick Evaluations" of Cultural Representation in AI

• URL: http://arxiv.org/abs/2503.19075v1
• Date: Mon, 24 Mar 2025 19:01:14 GMT
• Title: The Case for "Thick Evaluations" of Cultural Representation in AI
• Authors: Rida Qadri, Mark Diaz, Ding Wang, Michael Madaio,
• Abstract summary: Generative AI image models have been increasingly evaluated for their (in)ability to represent non-Western cultures.<n>We argue that these evaluations operate through reductive ideals of representation, neglecting how people define their own representation.<n>We introduce the idea of 'thick evaluations': a more granular, situated, and discursive measurement framework for evaluating representations of social worlds in AI images.
• Score: 11.53198252426806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI image models have been increasingly evaluated for their (in)ability to represent non-Western cultures. We argue that these evaluations operate through reductive ideals of representation, abstracted from how people define their own representation and neglecting the inherently interpretive and contextual nature of cultural representation. In contrast to these 'thin' evaluations, we introduce the idea of 'thick evaluations': a more granular, situated, and discursive measurement framework for evaluating representations of social worlds in AI images, steeped in communities' own understandings of representation. We develop this evaluation framework through workshops in South Asia, by studying the 'thick' ways in which people interpret and assign meaning to images of their own cultures. We introduce practices for thicker evaluations of representation that expand the understanding of representation underpinning AI evaluations and by co-constructing metrics with communities, bringing measurement in line with the experiences of communities on the ground.

Related papers

• Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs [7.802103248428407]
  We identify and test three assumptions behind current survey-based evaluation methods. We find a high level of instability across presentation formats, incoherence between evaluated versus held-out cultural dimensions, and erratic behavior under prompt steering.
  arXiv  Detail & Related papers  (2025-03-11T17:59:53Z)
• Diffusion Models Through a Global Lens: Are They Culturally Inclusive? [15.991121392458748]
  We introduce CultDiff benchmark, evaluating state-of-the-art diffusion models.<n>We show that these models often fail to generate cultural artifacts in architecture, clothing, and food, especially for underrepresented country regions.<n>We develop a neural-based image-image similarity metric, namely, CultDiff-S, to predict human judgment on real and generated images with cultural artifacts.
  arXiv  Detail & Related papers  (2025-02-13T03:05:42Z)
• CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries [63.00147630084146]
  Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding.<n>CultureVerse is a large-scale multimodal benchmark covering 19, 682 cultural concepts, 188 countries/regions, 15 cultural concepts, and 3 question types.<n>We propose CultureVLM, a series of VLMs fine-tuned on our dataset to achieve significant performance improvement in cultural understanding.
  arXiv  Detail & Related papers  (2025-01-02T14:42:37Z)
• Risks of Cultural Erasure in Large Language Models [4.613949381428196]
  We argue for the need of metricizable evaluations of language technologies that interrogate and account for historical power inequities.<n>We probe representations that a language model produces about different places around the world when asked to describe these contexts.<n>We analyze the cultures represented in the travel recommendations produced by a set of language model applications.
  arXiv  Detail & Related papers  (2025-01-02T04:57:50Z)
• Towards Automatic Evaluation for Image Transcreation [52.71090829502756]
  We propose a suite of automatic evaluation metrics inspired by machine translation (MT) metrics.<n>We identify three critical dimensions of image transcreation: cultural relevance, semantic equivalence and visual similarity.<n>Our results show that proprietary VLMs best identify cultural relevance and semantic equivalence, while vision-encoder representations are adept at measuring visual similarity.
  arXiv  Detail & Related papers  (2024-12-18T10:55:58Z)
• CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts [45.77570690529597]
  We introduce CROPE, a visual question answering benchmark designed to probe the knowledge of culture-specific concepts.<n>Our evaluation of several state-of-the-art open Vision and Language models shows large performance disparities between culture-specific and common concepts.<n>Experiments with contextual knowledge indicate that models struggle to effectively utilize multimodal information and bind culture-specific concepts to their depictions.
  arXiv  Detail & Related papers  (2024-10-20T17:31:19Z)
• Vision-Language Models under Cultural and Inclusive Considerations [53.614528867159706]
  Large vision-language models (VLMs) can assist visually impaired people by describing images from their daily lives. Current evaluation datasets may not reflect diverse cultural user backgrounds or the situational context of this use case. We create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind. We then evaluate several VLMs, investigating their reliability as visual assistants in a culturally diverse setting.
  arXiv  Detail & Related papers  (2024-07-08T17:50:00Z)
• Extrinsic Evaluation of Cultural Competence in Large Language Models [53.626808086522985]
  We focus on extrinsic evaluation of cultural competence in two text generation tasks. We evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts. We find weak correlations between text similarity of outputs for different countries and the cultural values of these countries.
  arXiv  Detail & Related papers  (2024-06-17T14:03:27Z)
• Towards Geographic Inclusion in the Evaluation of Text-to-Image Models [25.780536950323683]
  We study how much annotators in Africa, Europe, and Southeast Asia vary in their perception of geographic representation, visual appeal, and consistency in real and generated images. For example, annotators in different locations often disagree on whether exaggerated, stereotypical depictions of a region are considered geographically representative. We recommend steps for improved automatic and human evaluations.
  arXiv  Detail & Related papers  (2024-05-07T16:23:06Z)
• Towards Understanding and Mitigating Social Biases in Language Models [107.82654101403264]
  Large-scale pretrained language models (LMs) can be potentially dangerous in manifesting undesirable representational biases. We propose steps towards mitigating social biases during text generation. Our empirical results and human evaluation demonstrate effectiveness in mitigating bias while retaining crucial contextual information.
  arXiv  Detail & Related papers  (2021-06-24T17:52:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.