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There has been an explosion of growth in using AI, data science, and machine learning in all aspects of our daily life. There is a global competition among governments, industry, and academic institutions to lead research and development in this area. This paper discusses a novel multidisciplinary graduate education and research program at our institution to help develop a trained workforce to meet the demands required to understand and develop AI, data science and machine learning technologies. The program brings together faculty and students in engineering, computer science, and social science to build a traineeship program where cohort teams study fundamental and applied data science research, using compact modules across courses to personalize instruction and prepare each trainee with skills tailored to their prior experience and future career goals.
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Researcher reasoning meets computational capacity: Machine learning for social science
Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning amplifies researcher coding, summarizes complex data, relaxes statistical assumptions, and targets researcher attention to further social science research. We aim to reduce perceived barriers to machine learning by summarizing several fundamental building blocks and their grounding in classical statistics. We present a few guiding principles and promising approaches where we see particular potential for machine learning to transform social science inquiry. We conclude that machine learning tools are increasingly accessible, worthy of attention, and ready to yield new discoveries for social research.
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- Award ID(s):
- 2104607
- PAR ID:
- 10377063
- Date Published:
- Journal Name:
- Social science research
- Volume:
- 108
- ISSN:
- 0049-089X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.ssresearch.2022.102807
