Abstract

Understanding how Artificial Intelligence (AI) interfaces with rationality is crucial to ensure its continued benefit to society. Exploring the complexities introduced by AI's capacity to act rationally requires significant philosophical inquiry. This paper traces the historical evolution of the concept of rationality and discusses challenges in re-conceptualizing rationality within the context of AI. Rationality is the strategic use of knowledge to achieve objectives, with knowledge understood as "justified true belief" (Pinker 2021). Applying normative models of rationality, through disciplines such as logic, probability theory, and critical thinking, facilitates goal pursuit. Ancient philosophers emphasized self-examination and critical inquiry to uncover universal truths through empiricism (Plato 1998; Aristotle 2016). Renaissance philosophers introduced methodical doubt (Descartes 1993), while Enlightenment thinkers linked rationality with implications for human understanding and social change (Hegel 1977). Modern scholars focused on decision-making in complex environments (Simon 1965), and postmodern philosophers explored power dynamics between oppressor and oppressed (Foucault 1989). Moreover, the dynamic nature of rationality, characterized by adaptation and evolution over time, necessitates advanced machine learning techniques to imbue AI with flexibility and robustness (Bostrom 2014). Currently, the interplay between rationality and meaning is a central theme in philosophical inquiry. Human cognition, uniquely endowed with reasoning capacity, existential contemplation, and the pursuit of meaning, underscores the symbiotic relationship between rationality and the quest for existential significance (Frankl 1992). Understanding meaning is crucial for orienting AI to perform tasks like understanding data, selecting appropriate datasets and analysis approaches, ensuring it achieves objectives in beneficial and ethical ways. One challenge is how AI can discern the source of meaning, which necessitates understanding societal storytelling, symbols, and narratives, as it performs these tasks. This study advocates for a holistic reconceptualization of rationality, applicable to how AI discerns meaning when ensuring their outputs align with societal norms and values.

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