Foundation models are increasingly ubiquitous in our daily lives, used in
everyday tasks such as text-image searches, interactions with chatbots, and
content generation. As use increases, so does concern over the disparities in
performance and fairness of these models for different people in different
parts of the world. To assess these growing regional disparities, we present
World Wide Dishes, a mixed text and image dataset consisting of 765 dishes,
with dish names collected in 131 local languages. World Wide Dishes has been
collected purely through human contribution and decentralised means, by
creating a website widely distributed through social networks. Using the
dataset, we demonstrate a novel means of operationalising capability and
representational biases in foundation models such as language models and
text-to-image generative models. We enrich these studies with a pilot community
review to understand, from a first-person perspective, how these models
generate images for people in five African countries and the United States.
We find that these models generally do not produce quality text and image
outputs of dishes specific to different regions. This is true even for the US,
which is typically considered to be more well-resourced in training data -
though the generation of US dishes does outperform that of the investigated
African countries. The models demonstrate a propensity to produce outputs that
are inaccurate as well as culturally misrepresentative, flattening, and
insensitive. These failures in capability and representational bias have the
potential to further reinforce stereotypes and disproportionately contribute to
erasure based on region. The dataset and code are available at
this https URL

通过 World Wide Dishes 数据集和社区回顾，研究揭示了基于语言模型和文本 - 图像生成模型的基础模型在生成特定地区美食的文本和图像输出时存在的能力和表现偏差问题。

以地域多样的世界美食数据集为基础，为基础模型提供饮食

You are what you eat? Feeding foundation models a regionally diverse  food dataset of World Wide Dishes

As machine learning methods are deployed in real-world settings such as
healthcare, legal systems, and social science, it is crucial to recognize how
they shape social biases and stereotypes in these sensitive decision-making
processes. Among such real-world deployments are large-scale pretrained
language models (LMs) that can be potentially dangerous in manifesting
undesirable representational biases - harmful biases resulting from
stereotyping that propagate negative generalizations involving gender, race,
religion, and other social constructs. As a step towards improving the fairness
of LMs, we carefully define several sources of representational biases before
proposing new benchmarks and metrics to measure them. With these tools, 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 for high-fidelity text
generation, thereby pushing forward the performance-fairness Pareto frontier.

为了提高语言模型的公正性，本文提出了多项定义并给出了新的测试和度量方式，旨在减少机器学习在文本生成过程中对性别、种族、语言宗教等社会构建的刻板印象所带来的负面影响。实证结果和人工评估表明，该方法能够在文本生成中减少偏见同时保留重要的上下文信息。

探索并缓解语言模型中的社会偏见

Towards Understanding and Mitigating Social Biases in Language Models

Rising concern for the societal implications of artificial intelligence
systems has inspired demands for greater transparency and accountability.
However the datasets which empower machine learning are often used, shared and
re-used with little visibility into the processes of deliberation which led to
their creation. Which stakeholder groups had their perspectives included when
the dataset was conceived? Which domain experts were consulted regarding how to
model subgroups and other phenomena? How were questions of representational
biases measured and addressed? Who labeled the data? In this paper, we
introduce a rigorous framework for dataset development transparency which
supports decision-making and accountability. The framework uses the cyclical,
infrastructural and engineering nature of dataset development to draw on best
practices from the software development lifecycle. Each stage of the data
development lifecycle yields a set of documents that facilitate improved
communication and decision-making, as well as drawing attention the value and
necessity of careful data work. The proposed framework is intended to
contribute to closing the accountability gap in artificial intelligence
systems, by making visible the often overlooked work that goes into dataset
creation.

通过严格的数据集开发透明度框架，从软件开发生命周期中提取最佳实践，以决策和问责支持来关闭人工智能系统中的问责差距。