At the heart of what drives the bulk of innovation and activity in Silicon
Valley and elsewhere is scalability. This unwavering commitment to scalability
-- to identify strategies for efficient growth -- is at the heart of what we
refer to as "scale thinking." Whether people are aware of it or not, scale
thinking is all-encompassing. It is not just an attribute of one's product,
service, or company, but frames how one thinks about the world (what
constitutes it and how it can be observed and measured), its problems (what is
a problem worth solving versus not), and the possible technological fixes for
those problems. This paper examines different facets of scale thinking and its
implication on how we view technology and collaborative work. We argue that
technological solutions grounded in scale thinking are unlikely to be as
liberatory or effective at deep, systemic change as their purveyors imagine.
Rather, solutions which resist scale thinking are necessary to undo the social
structures which lie at the heart of social inequality. We draw on recent work
on mutual aid networks and propose questions to ask of collaborative work
systems as a means to evaluate technological solutions and guide designers in
identifying sites of resistance to scale thinking.

研究探讨了可扩展性思维的不同方面及其对如何看待技术和协作工作的影响。作者认为，基于可扩展性思维的技术解决方案不太可能像它们的传播者想象的那样解决社会不平等问题，而需要抵制可扩展性思维的解决方案来消除社会结构上的不平等。作者提出了一些问题，作为评价技术解决方案和指导设计者识别抵抗可扩展性思维的场所的方法。

反对规模化：规模思考的挑战和抗拒

Against Scale: Provocations and Resistances to Scale Thinking

When consequential decisions are informed by algorithmic input, individuals
may feel compelled to alter their behavior in order to gain a system's
approval. Models of agent responsiveness, termed "strategic manipulation,"
analyze the interaction between a learner and agents in a world where all
agents are equally able to manipulate their features in an attempt to "trick" a
published classifier. In cases of real world classification, however, an
agent's ability to adapt to an algorithm is not simply a function of her
personal interest in receiving a positive classification, but is bound up in a
complex web of social factors that affect her ability to pursue certain action
responses. In this paper, we adapt models of strategic manipulation to capture
dynamics that may arise in a setting of social inequality wherein candidate
groups face different costs to manipulation. We find that whenever one group's
costs are higher than the other's, the learner's equilibrium strategy exhibits
an inequality-reinforcing phenomenon wherein the learner erroneously admits
some members of the advantaged group, while erroneously excluding some members
of the disadvantaged group. We also consider the effects of interventions in
which a learner subsidizes members of the disadvantaged group, lowering their
costs in order to improve her own classification performance. Here we encounter
a paradoxical result: there exist cases in which providing a subsidy improves
only the learner's utility while actually making both candidate groups
worse-off--even the group receiving the subsidy. Our results reveal the
potentially adverse social ramifications of deploying tools that attempt to
evaluate an individual's "quality" when agents' capacities to adaptively
respond differ.

本文研究算法决策，分析了在社会不平等的情况下，一方面面临更高的成本的候选群体可能会被错误地排除，而另一方面面临更低成本的候选群体可能会被错误地接受。本文还探讨了给予补贴的干预效果，发现某些情况下给予补贴只会提高算法决策者的效用，但实际上会损害所有候选群体的利益，包括补贴接收者。