Artificial Intelligence and Machine Learning present unique challenges and opportunities for knowledge production, accumulation, and dissemination in the social sciences. AI and Deep Machine Learning is indeed redefining what counts as knowledge making, consuming, and fabricating.
Now and more so in the coming decades, AI will rapidly transform citizen expectations and social norms, the marketplace and economies, political and legal processes, and governmental and non-governmental institutions. The governance challenges and opportunities associated with AI technologies are formidable and unprecedented, as policies, regulations, and laws seldom stay abreast of transformative technological innovation.
A good number of social science departments and research institutes in the US and around the world have recently launched serious efforts in addressing the challenges, implications, and ramifications of AI in social sciences and cognate fields. Yet it is rare to find an institution that is seeking to also address the reverse: that is, how does AI and Deep Machine Learning practically transform, epistemologically and methodologically, the production, accumulation, and dissemination of knowledge in social sciences? How do we channel the tremendous advances already achieved in AI and Deep Machine Learning to produce and verify knowledge in social sciences? AI and Deep Machine Learning has already revolutionized many fields of knowledge (such as medical research and natural language processing, not to mention engineering, fields). The social sciences must take a leadership role, especially at UF where AI is indeed becoming its trademark given the tremendous computing capabilities that the university possesses through the HiPerGator. Research clusters on AI and Deep Machine Learning in the social sciences will well position UF’s CLAS as a pioneer in the field.