In her influential 1988 paper, Situated Knowledges, Donna Haraway uses vision
and perspective as a metaphor to discuss scientific knowledge. Today,
egocentric computer vision discusses many of the same issues, except in a
literal vision context. In this short position paper, we collapse that
metaphor, and explore the interactions between feminist epistemology and
egocentric CV as "Egocentric Epistemology." Using this framework, we argue for
the use of qualitative, human-centric methods as a complement to performance
benchmarks, to center both the literal and metaphorical perspective of human
crowd workers in CV.

研究以人为中心的方法来补充计算机视觉中心方法，结合雌性主义认识论与人类的视角构建 “Egocentric 认识论”，促进人工智能技术与人文关怀的结合。

依据情境的摄像头与知识：计算机视觉中一种以自我为中心的认识论

Situated Cameras, Situated Knowledges: Towards an Egocentric  Epistemology for Computer Vision

As the public seeks greater accountability and transparency from machine
learning algorithms, the research literature on methods to explain algorithms
and their outputs has rapidly expanded. Feature importance methods form a
popular class of explanation methods. In this paper, we apply the lens of
feminist epistemology to recent feature importance research. We investigate
what epistemic values are implicitly embedded in feature importance methods and
how or whether they are in conflict with feminist epistemology. We offer some
suggestions on how to conduct research on explanations that respects feminist
epistemic values, taking into account the importance of social context, the
epistemic privileges of subjugated knowers, and adopting more interactional
ways of knowing.

本研究探讨了特征重要性方法中的认识价值隐含问题，并提出了在考虑社会背景，被压迫知识者的认识优势和采用更多互动性知识方式的前提下，如何进行尊重女性主义认识论价值的解释研究方面的建议。