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

An emerging solution for explaining Transformer-based models is to use
vector-based analysis on how the representations are formed. However, providing
a faithful vector-based explanation for a multi-layer model could be
challenging in three aspects: (1) Incorporating all components into the
analysis, (2) Aggregating the layer dynamics to determine the information flow
and mixture throughout the entire model, and (3) Identifying the connection
between the vector-based analysis and the model's predictions. In this paper,
we present DecompX to tackle these challenges. DecompX is based on the
construction of decomposed token representations and their successive
propagation throughout the model without mixing them in between layers.
Additionally, our proposal provides multiple advantages over existing solutions
for its inclusion of all encoder components (especially nonlinear feed-forward
networks) and the classification head. The former allows acquiring precise
vectors while the latter transforms the decomposition into meaningful
prediction-based values, eliminating the need for norm- or summation-based
vector aggregation. According to the standard faithfulness evaluations, DecompX
consistently outperforms existing gradient-based and vector-based approaches on
various datasets. Our code is available at
this https URL

本文提出了 DecompX，通过构建分解的令牌表示形式和它们在模型中的连续传播来解决使用矢量分析解释多层 Transformer 模型挑战的难题，并证明其在各种数据集上始终优于现有的梯度和矢量分析方法。

DecompX：通过传播标记分解来解释 Transforme 决策

DecompX: Explaining Transformers Decisions by Propagating Token  Decomposition

Convolutional neural networks (CNN) have led to many state-of-the-art results
spanning through various fields. However, a clear and profound theoretical
understanding of the forward pass, the core algorithm of CNN, is still lacking.
In parallel, within the wide field of sparse approximation, Convolutional
Sparse Coding (CSC) has gained increasing attention in recent years. A
theoretical study of this model was recently conducted, establishing it as a
reliable and stable alternative to the commonly practiced patch-based
processing. Herein, we propose a novel multi-layer model, ML-CSC, in which
signals are assumed to emerge from a cascade of CSC layers. This is shown to be
tightly connected to CNN, so much so that the forward pass of the CNN is in
fact the thresholding pursuit serving the ML-CSC model. This connection brings
a fresh view to CNN, as we are able to attribute to this architecture
theoretical claims such as uniqueness of the representations throughout the
network, and their stable estimation, all guaranteed under simple local
sparsity conditions. Lastly, identifying the weaknesses in the above pursuit
scheme, we propose an alternative to the forward pass, which is connected to
deconvolutional, recurrent and residual networks, and has better theoretical
guarantees.

提出了一种新的多层模型，ML-CSC，其信号被假定为从一系列 CSC 层中出现。CNN 的前向传递实际上是服务于 ML-CSC 模型的阈值追踪，将 CNN 与 ML-CSC 模型紧密联系起来，为 CNN 带来了新的视角，同时也提出了一个与去卷积网络相关的前向通道替代方案，具有更好的理论保证。