Despite a proliferation of abstract guidelines, codes of conduct, and theoretical interventions, advances in the field of artificial intelligence (AI) ethics have had little practical effect on the behavior of technology companies. Although empirical work in AI ethics is limited, what evidence does exist suggests the impact of AI ethics is constrained by existing value structures in the technology industry, intra-organizational work processes and incentives, and resource constraints.
Thus, for AI ethical frameworks and tools to be effective in real-world contexts, they must be developed with a deep understanding of organizational factors that may influence their practicality or efficacy. In this research, we use a qualitative approach to better elucidate internal and external factors within early-stage startups that influence how these companies think about and develop AI- and ML-enabled technologies. In this talk, I will discuss preliminary results for one of these factors–the need for capital–and discuss some of the implications for AI ethics.
Amy’s research focuses on human-algorithm interactions, taking into consideration how humans both shape and react to algorithms. She conducts research on how psychological, social, and institutional forces shape how entrepreneurs develop algorithmic systems as well as how algorithmic systems adapt to idiosyncratic, user-level behavior and broader social influences over time. She uses a combination of qualitative and quantitative empirical methods as well as simulation.
Amy received her Ph.D. in psychology and neuroscience from Duke University. After graduate school, she was an assistant professor at Bard College, where she taught neuroscience, abnormal psychology, and research methods. After leaving academia, she conducted research and developed machine learning models for government agencies such as DARPA and the U.S. Air Force to explain and predict human behavior. As a senior data scientist at True Fit and Chewy, she developed product recommendation and search systems. She also conducted quantitative user research to assess how users’ psychology informs their evaluation of algorithmic predictions. Amy is passionate about diversity and inclusion in the technology industry.