Founder, CEO, Acesio Inc.
Head of Behavioral and Organizational Research, Acesio Inc.
The focus of this paper is to investigate deep learning algorithm development in an early stage start-up in which edges of knowledge formation and organizational formation were unsettled and contested. We use a debate by anthropologists Clifford Geertz and Claude Levi-Strauss to examine these contested computational forms of knowledge through a contemporary lens. We set out to explore these epistemological edges as they shift over time and as they have real practical implications in how expertise and people are valued as useful or non-useful, integrated or rejected by the practice of deep learning algorithm R&D. We discuss the nuances of epistemic silences and acknowledgments of domain knowledge and universalizing machine learning knowledge in an organization that was rapidly attempting to develop algorithms for diagnostic insights. We conclude with reflections on how an AI-Inflected Ethnography perspective may emerge from both, data science and anthropology perspectives together, and what such a perspective may imply for a future of AI organizational formation, for the people who build algorithms and for a certain kind of research labor that AI inflection suggests.
Keywords: AI, Deep Learning, Algorithm R&D, Epistemology, Domain Knowledge