被抽象化：抵制AI研究社区中的异化与无根基抽象
Abstracted Away: Resisting Alienation and Ungrounded Abstraction in AI Research Communities
浏览论文内容
中文总结 AI 辅助
三名早期职业批判性AI研究者通过自我民族志探究构建异化解释框架，指出情感抽象是异化机制之一，提出关注情感反应、集体行动可抵制异化，该框架可作为促进AI研究包容性的解释学资源。
中文摘要 AI 辅助
计算AI研究中的抽象逻辑常常将重要形式的知识与反思置于次要位置：主流的合法性标准与受伤害的生活经验相分离；工作目标与将其付诸实践的操作方式不匹配；职业需求挤占了批判性反思。尽管此前已有学术及面向社区的努力试图重新定位并挑战常见实践，但社会技术伤害与认知不公的情况依然存在。作为三名早期职业的批判性AI研究者，我们将此体验为异化：感觉自己是研究社区中的局外人。这种异化表现为我们背景的某些方面被忽视，而另一些方面被符号化。我们认为，这种异化通过与抽象相类似的机制发生，即通过与相关的物质现实拉开距离。除了抽象作为构建复杂计算任务的基础实践在计算AI研究中发挥作用外，我们还将其视为计算研究空间中的一种社会规范，这一点通过对我们的异化进行的自我民族志探究得以说明。我们叙述了三个小插曲，描述了我们如何在研究社区中遭遇并抵制异化。通过分析这些叙述中的主题，我们构建了一个异化的解释框架，对其前提、机制和危害进行了分类。最后，我们指出情感抽象（affect abstraction）是我们所描述的异化机制之一，这是一种高杠杆性的机制，可通过关注我们的情感反应来抵制，而集体行动则是抵制时降低风险与孤立感的一种方式。为了帮助他人进行类似反思，我们将该框架作为一种解释学资源呈现。批判性自我反思与意义建构是挑战排他性学科规范、培育更具包容性的AI研究形式的必要步骤。
英文摘要
Logics of abstraction in computational AI research often push important forms of knowledge and reflection aside: dominant standards of legitimacy separate from lived experience of harm; the goals of work misalign with the practices that operationalize them; and career demands crowd out critical reflection. Even as prior academic and community-oriented efforts have sought to recontextualize and challenge common practices, exposure to sociotechnical harms and epistemic injustice persists. As three early-career critical AI researchers, we experienced this as alienation: feeling like outsiders in our research communities. This alienation has involved having some aspects of our backgrounds overlooked and others tokenized. We argue our alienation occurred through mechanisms that mirror abstraction by creating distance from relevant material realities. Beyond abstraction's role in computational AI research as a foundational practice structuring complex computational tasks, we have encountered it as a social norm in computational research spaces, illustrated through an autoethnographic inquiry into our alienation. We narrate three vignettes describing how we encountered and resisted alienation in our research communities. By analyzing themes across these accounts, we construct an interpretive framework of alienation categorizing its preconditions, mechanisms, and harms. Finally, we identify affect abstraction, one of the mechanisms of alienation we describe, as a high-leverage mechanism that is resistible by staying attuned to our affective responses, and collective action as a way to reduce risk and isolation when engaging in resistance. To assist others with similar reflection, we present our framework as a hermeneutic resource. Critical self-reflection and meaning-making are necessary steps toward challenging exclusionary disciplinary norms and cultivating more inclusive forms of AI research.
发表机构
- Cornell University（康奈尔大学）
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
