Fairness, Bias, and Appropriate Use of Machine Learning

Artificial Intelligence and Machine Learning are increasingly being used to automate decision-making in many sectors within international development. Although computer intelligence is continuously improving, it has been shown that improper implementation of these algorithms can lead to strong bias, unfairness, or exclusion of certain groups. This research project helps determine guidelines of ethical use of machine learning in developing countries, developing a framework of use of machine learning with criteria of fairness and appropriate use, discovering partnerships in industry, academia, or government in developing countries, and building capacity through educational materials and datasets shared with the world at the end of the research. Integral to this effort are case studies of several sites abroad and in the US which focus on different aspects of applications of machine learning, from employment, to medicine, education, lending, devices, to name a few. The output of this research includes a framework for appropriate and ethical use of machine learning methods based on the interdisciplinary case studies, data analyses, meta-analyses, and pedagogical materials which can be integrated into future machine learning courses around the world.