Collective action in Machine Learning is the study of the control that a
coordinated group can have over machine learning algorithms. While previous
research has concentrated on assessing the impact of collectives against Bayes
optimal classifiers, this perspective is limited, given that in reality,
classifiers seldom achieve Bayes optimality and are influenced by the choice of
learning algorithms along with their inherent inductive biases. In this work,
we initiate the study of how the choice of the learning algorithm plays a role
in the success of a collective in practical settings. Specifically, we focus on
distributionally robust algorithms (DRO), popular for improving a worst group
error, and on the popular stochastic gradient descent (SGD), due to its
inductive bias for "simpler" functions. Our empirical results, supported by a
theoretical foundation, show that the effective size and success of the
collective are highly dependent on properties of the learning algorithm. This
highlights the necessity of taking the learning algorithm into account when
studying the impact of collective action in Machine learning.

学习算法选择对于集体行动在机器学习中的影响至关重要。本研究通过实证结果和理论分析，展示了集体的有效性和成功性与学习算法的属性密切相关，强调了在研究机器学习中的集体行动影响时，考虑学习算法的必要性。

学习算法在集体行动中的角色

The Role of Learning Algorithms in Collective Action

Research on algorithmic recourse typically considers how an individual can
reasonably change an unfavorable automated decision when interacting with a
fixed decision-making system. This paper focuses instead on the online setting,
where system parameters are updated dynamically according to interactions with
data subjects. Beyond the typical individual-level recourse, the online setting
opens up new ways for groups to shape system decisions by leveraging the
parameter update rule. We show empirically that recourse can be improved when
users coordinate by jointly computing their feature perturbations, underscoring
the importance of collective action in mitigating adverse automated decisions.

该论文研究算法性救济，侧重于在线环境中动态更新系统参数并通过协同计算特征扰动的集体行动来改善不利的自动化决策。

在线算法补救的集体行动

Online Algorithmic Recourse by Collective Action

Whether in groups of humans or groups of computer agents, collaboration is
most effective between individuals who have the ability to coordinate on a
joint strategy for collective action. However, in general a rational actor will
only intend to coordinate if that actor believes the other group members have
the same intention. This circular dependence makes rational coordination
difficult in uncertain environments if communication between actors is
unreliable and no prior agreements have been made. An important normative
question with regard to coordination in these ad hoc settings is therefore how
one can come to believe that other actors will coordinate, and with regard to
systems involving humans, an important empirical question is how humans arrive
at these expectations. We introduce an exact algorithm for computing the
infinitely recursive hierarchy of graded beliefs required for rational
coordination in uncertain environments, and we introduce a novel mechanism for
multiagent coordination that uses it. Our algorithm is valid in any environment
with a finite state space, and extensions to certain countably infinite state
spaces are likely possible. We test our mechanism for multiagent coordination
as a model for human decisions in a simple coordination game using existing
experimental data. We then explore via simulations whether modeling humans in
this way may improve human-agent collaboration.

在不确定的环境中，对于合作行为的协调最有效的方式是通过相互理解并达成共识。我们提出了一种精确的算法来计算在不确定环境下合理协调所需的无限递归分级信念，并引入了一种新的多智能体协调机制。我们测试了该机制作为人的决策模型的效果，并发现这种方式可以提高人 - 智能体的协同效率。

人类临时协调建模

Modeling Human Ad Hoc Coordination

We examine the growth, survival, and context of 256 novel hashtags during the
2012 U.S. presidential debates. Our analysis reveals the trajectories of
hashtag use fall into two distinct classes: "winners" that emerge more quickly
and are sustained for longer periods of time than other "also-rans" hashtags.
We propose a "conversational vibrancy" framework to capture dynamics of
hashtags based on their topicality, interactivity, diversity, and prominence.
Statistical analyses of the growth and persistence of hashtags reveal novel
relationships between features of this framework and the relative success of
hashtags. Specifically, retweets always contribute to faster hashtag adoption,
replies extend the life of "winners" while having no effect on "also-rans."
This is the first study on the lifecycle of hashtag adoption and use in
response to purely exogenous shocks. We draw on theories of uses and
gratification, organizational ecology, and language evolution to discuss these
findings and their implications for understanding social influence and
collective action in social media more generally.

研究分析了 2012 年美国总统辩论期间 256 个新兴 hashtags 的生长、生存和背景，发现基于话题性、交互性、多样性和知名度的 “对话活力” 框架可捕捉 hashtags 的动态。统计分析结果显示，转发可以促进 hashtags 更快地被采用，回复对 “优胜者” 的生命周期有延长作用，但对 “落后者” 无影响，这些结果对于更好地理解社交媒体中的社会影响力和集体行动具有重要的启示和影响。