AI is an interdisciplinary field that draws on many fields of study, including computer science, psychology, neuroscience, and mathematics. AI researchers often bring a social science perspective to the field. They want to understand the social implications of AI and to identify ways that humans can best interact with AI systems. Social scientists look at how people interact with AI systems and how these interactions might change over time. Social scientists also want to understand how AI systems learn from human behavior and make predictions about how people will behave in the future.
There are two main types of social science approaches to AI. The first is an approach that focuses on the ethical implications of AI. This approach assumes that we cannot build AI without carefully considering its ethical implications for society. The second type of social science approach is an approach that looks at the psychological impact of AI on humans. This approach assumes that humans could feel uncomfortable or threatened by the presence of AI in their lives. Social scientists use these approaches to help us better understand and predict the impact of AI on society as a whole.
Emnet Tafesse and Ranjit Singh contrast the differences in how researchers in the “Global South” and “Global North” investigate the social impacts of artificial intelligence.
Emnet Tafesse is a Research Analyst at Data & Society on the AI on the Ground Initiative. She has a passion for utilizing advocacy, research, and policy to create positive social change and a more equitable world. She received her Master’s in Public Policy from the University of Chicago and her BA in Political Science and Sociology from Howard University.
Ranjit has a doctorate in Science and Technology Studies (STS) from Cornell University. His research lies at the intersection of data infrastructures, global development, and public policy. He uses methods of interview-based qualitative sociology and multi-sited ethnography in his research. He examines the everyday experiences of people subject to data-driven practices and follows the mutual shaping of their lives and their data records.