The unprecedented photorealistic results achieved by recent text-to-image
generative systems and their increasing use as plug-and-play content creation
solutions make it crucial to understand their potential biases. In this work,
we introduce three indicators to evaluate the realism, diversity and
prompt-generation consistency of text-to-image generative systems when prompted
to generate objects from across the world. Our indicators complement
qualitative analysis of the broader impact of such systems by enabling
automatic and efficient benchmarking of geographic disparities, an important
step towards building responsible visual content creation systems. We use our
proposed indicators to analyze potential geographic biases in state-of-the-art
visual content creation systems and find that: (1) models have less realism and
diversity of generations when prompting for Africa and West Asia than Europe,
(2) prompting with geographic information comes at a cost to prompt-consistency
and diversity of generated images, and (3) models exhibit more region-level
disparities for some objects than others. Perhaps most interestingly, our
indicators suggest that progress in image generation quality has come at the
cost of real-world geographic representation. Our comprehensive evaluation
constitutes a crucial step towards ensuring a positive experience of visual
content creation for everyone.

通过引入三个指标，我们评估了文本到图像生成系统在生成全球各地对象时的真实性、多样性和提示生成的一致性，分析了当前最先进的视觉内容生成系统中可能存在的地理偏见，并发现：（1）在提示生成非洲和西亚的对象时，模型的真实性和生成多样性较欧洲较低，（2）使用地理信息作为提示对生成的图片的一致性和多样性具有一定的负面影响，以及（3）在一些对象上，模型存在更多的区域级差异。引入这些指标是确保每个人在视觉内容创建中有积极体验的关键步骤。

评估图像生成中的地理多样性差异

DIG In: Evaluating Disparities in Image Generations with Indicators for  Geographic Diversity

Generative, ML-driven interactive systems have the potential to change how
people interact with computers in creative processes - turning tools into
co-creators. However, it is still unclear how we might achieve effective
human-AI collaboration in open-ended task domains. There are several known
challenges around communication in the interaction with ML-driven systems. An
overlooked aspect in the design of co-creative systems is how users can be
better supported in learning to collaborate with such systems. Here we reframe
human-AI collaboration as a learning problem: Inspired by research on team
learning, we hypothesize that similar learning strategies that apply to
human-human teams might also increase the collaboration effectiveness and
quality of humans working with co-creative generative systems. In this position
paper, we aim to promote team learning as a lens for designing more effective
co-creative human-AI collaboration and emphasize collaboration process quality
as a goal for co-creative systems. Furthermore, we outline a preliminary
schematic framework for embedding team learning support in co-creative AI
systems. We conclude by proposing a research agenda and posing open questions
for further study on supporting people in learning to collaborate with
generative AI systems.

本研究将人工智能与人类合作的问题重新定义为一种学习问题，提出团队学习策略可提高合作效果和质量，为设计更高效的人工智能合作系统提供了新的视角和框架，并就支持人们学习如何与生成型人工智能系统协作的进一步研究提出了问题和议程。