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

Participatory approaches to artificial intelligence (AI) and machine learning
(ML) are gaining momentum: the increased attention comes partly with the view
that participation opens the gateway to an inclusive, equitable, robust,
responsible and trustworthy AI.Among other benefits, participatory approaches
are essential to understanding and adequately representing the needs, desires
and perspectives of historically marginalized communities. However, there
currently exists lack of clarity on what meaningful participation entails and
what it is expected to do. In this paper we first review participatory
approaches as situated in historical contexts as well as participatory methods
and practices within the AI and ML pipeline. We then introduce three case
studies in participatory AI.Participation holds the potential for beneficial,
emancipatory and empowering technology design, development and deployment while
also being at risk for concerns such as cooptation and conflation with other
activities. We lay out these limitations and concerns and argue that as
participatory AI/ML becomes in vogue, a contextual and nuanced understanding of
the term as well as consideration of who the primary beneficiaries of
participatory activities ought to be constitute crucial factors to realizing
the benefits and opportunities that participation brings.

本文综述了 AI/ML 管道内的参与方法和做法，在介绍三个案例的同时探讨了参与 AI 的局限性和担忧，讨论了参与活动的主要受益者和参与在设计，开发和部署中的作用。

人民的力量？参与式人工智能的机遇与挑战

Power to the People? Opportunities and Challenges for Participatory AI

Despite growing calls for participation in AI design, there are to date few
empirical studies of what these processes look like and how they can be
structured for meaningful engagement with domain experts. In this paper, we
examine a notable yet understudied AI design process in the legal domain that
took place over a decade ago, the impact of which still informs legal
automation efforts today. Specifically, we examine the design and evaluation
activities that took place from 2006 to 2011 within the TeXT Retrieval
Conference's (TREC) Legal Track, a computational research venue hosted by the
National Institute of Standards and Technologies. The Legal Track of TREC is
notable in the history of AI research and practice because it relied on a range
of participatory approaches to facilitate the design and evaluation of new
computational techniques--in this case, for automating attorney document review
for civil litigation matters. Drawing on archival research and interviews with
coordinators of the Legal Track of TREC, our analysis reveals how an
interactive simulation methodology allowed computer scientists and lawyers to
become co-designers and helped bridge the chasm between computational research
and real-world, high-stakes litigation practice. In analyzing this case from
the recent past, our aim is to empirically ground contemporary critiques of AI
development and evaluation and the calls for greater participation as a means
to address them.

本文回顾了 2006 年至 2011 年期间由 TeXT 数据库检索会议主持的 Legal Track 内发生的几个值得研究的 AI 设计和评估活动，并从文档检索领域中的律师文档审查自动化设计方法中总结了一种交互式仿真方法，该方法帮助计算机科学家和律师成为共同设计师，并促进了计算机研究与实际高风险诉讼实践之间的沟通。