Growing interest and investment in the capabilities of foundation models has
positioned such systems to impact a wide array of public services. Alongside
these opportunities is the risk that these systems reify existing power
imbalances and cause disproportionate harm to marginalized communities.
Participatory approaches hold promise to instead lend agency and
decision-making power to marginalized stakeholders. But existing approaches in
participatory AI/ML are typically deeply grounded in context - how do we apply
these approaches to foundation models, which are, by design, disconnected from
context? Our paper interrogates this question.
First, we examine existing attempts at incorporating participation into
foundation models. We highlight the tension between participation and scale,
demonstrating that it is intractable for impacted communities to meaningfully
shape a foundation model that is intended to be universally applicable. In
response, we develop a blueprint for participatory foundation models that
identifies more local, application-oriented opportunities for meaningful
participation. In addition to the "foundation" layer, our framework proposes
the "subfloor'' layer, in which stakeholders develop shared technical
infrastructure, norms and governance for a grounded domain, and the "surface''
layer, in which affected communities shape the use of a foundation model for a
specific downstream task. The intermediate "subfloor'' layer scopes the range
of potential harms to consider, and affords communities more concrete avenues
for deliberation and intervention. At the same time, it avoids duplicative
effort by scaling input across relevant use cases. Through three case studies
in clinical care, financial services, and journalism, we illustrate how this
multi-layer model can create more meaningful opportunities for participation
than solely intervening at the foundation layer.

参与型基础模型可以有效地将权力和决策能力赋予边缘化群体，避免现有的权力不平衡以及对弱势社区的不成比例伤害，并通过多层模型在临床照护、金融服务和新闻等领域创造更具意义的参与机会。

基础模型时代的参与

Participation in the age of foundation models

The present paper is about the participation of our team "techno" on
CERIST'22 shared tasks. We used an available dataset "task1.c" related to
covid-19 pandemic. It comprises 4128 tweets for sentiment analysis task and
8661 tweets for fake news detection task. We used natural language processing
tools with the combination of the most renowned pre-trained language models
BERT (Bidirectional Encoder Representations from Transformers). The results
shows the efficacy of pre-trained language models as we attained an accuracy of
0.93 for the sentiment analysis task and 0.90 for the fake news detection task.

我们的团队 “techno” 参加了 CERIST'22 共享任务，利用自然语言处理工具和 BERT 预训练语言模型，对与 COVID-19 疫情有关的 4128 个推文进行了情感分析和 8661 个推文进行了虚假新闻检测任务，并获得了情感分析任务 0.93 的准确度和虚假新闻检测任务 0.90 的准确度。

基于 BERT 模型的 COVID-19 相关推文的虚假新闻检测和情感分析

Classifying COVID-19 Related Tweets for Fake News Detection and  Sentiment Analysis with BERT-based Models

A conversation corpus is essential to build interactive AI applications.
However, the demographic information of the participants in such corpora is
largely underexplored mainly due to the lack of individual data in many
corpora. In this work, we analyze a Korean nationwide daily conversation corpus
constructed by the National Institute of Korean Language (NIKL) to characterize
the participation of different demographic (age and sex) groups in the corpus.

构建交互式人工智能应用程序必需有对话语料库，本文分析了韩国全国性的日常对话语料库，利用不同年龄和性别群体的参与表征了该语料库的人口统计学信息。

谁在缺席？对韩国全国日常对话语料库中不同人口群体参与情况的描述

Who Is Missing? Characterizing the Participation of Different Demographic Groups in a Korean Nationwide Daily Conversation Corpus

This paper critically examines existing modes of participation in design
practice and machine learning. Cautioning against 'participation-washing', it
suggests that the ML community must become attuned to possibly exploitative and
extractive forms of community involvement and shift away from the prerogatives
of context-independent scalability.

论文批判性地审视了设计实践和机器学习中现有的参与模式，提醒需要注意可能存在的剥削性和提取性的社区参与形式，并转向考虑上下文无关可扩展性的优先权。