Applying Epistemology to AI in the Social Arena: Exploring Fundamental Considerations

Authors: Gianni Jacucci

Abstract:

Epistemology traditionally finds its place within human research philosophies and methodologies. Artificial Intelligence (AI) methods pose challenges, particularly given the unresolved relationship between AI and pivotal concepts in social arenas such as hermeneutics and accountability. We begin by examining the essential criteria governing scientific rigor in the human sciences. We revisit the three foundational philosophies underpinning qualitative research methods: empiricism, hermeneutics, and phenomenology. We elucidate the distinct attributes, merits, and vulnerabilities inherent in the methodologies they inspire. The integration of AI, e.g., deep learning algorithms, sparks an interest in evaluating these criteria against the diverse forms of AI architectures. For instance, Interpreted AI could be viewed as a hermeneutic approach, relying on a priori interpretations, while “straight” AI may be perceived as a descriptive phenomenological approach, processing original and uncontaminated data. And the incremental learning in chatbots like ChatGPT echoes the empiricist theory growth in grounded theory. This paper serves as groundwork for such explorations, offering preliminary reflections to lay the foundation and outline the initial landscape.

Keywords: Artificial intelligence, deep learning, epistemology, qualitative research, methodology, hermeneutics, accountability.

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