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. Learning from and collaborating with partners applying machine learning in emerging economies are essential guiding principles for this work.

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Exploring Fairness in Machine Learning for International Development


The team is from MIT, Harvard, and Boston College and is under the umbrella of MIT D-Lab's Comprehensive Initiative on Technology Evaluation.

Faculty Principal Investigator, Dr. Daniel Frey
Professor of Mechanical Engineering, CITE Director, Faculty Research Director of MIT D-Lab and the Co-Director of Experimental Design research in the SUTD-MIT International Design Center. Read more.

Project Manager, Dr. Rich Fletcher
Research Scientist at MIT D-Lab and an Assistant Professor at University of Massachusetts Medical School, leads the Mobile Technology Group at MIT D-Lab, applying machine learning and innovative sensors for health diagnostics. Read more.
Machine Learning Lead Researcher, Dr. Maryam Najafian
Research Scientist at MIT Computer Science and AI Lab (CSAIL) and Institution for Data, Science, and Society (IDDS). Read more.

Lead Economics Investigator, Dr. Mike Teodorescu
Tenure-track Assistant Professor of Information Systems at Boston College’s Carroll School of Management. Read more.

Economics consultant, Daniel Albert Brown
Behavior Economist, and Doctoral candidate in the Management unit at Harvard Business School where he researches the psychological effects of value and performance measurement in organizations. Read more.

Research Assistants

Amit Gandhi
PhD candidate at MIT in mechanical engineering, Co-Instructor of D-Lab energy, and co-founder of Sensen, an affordable plug and play solution for remote data collection. Read more.

Yazeed Awwad
Masters student at MIT Computer Science and Electrical Engineering, specializing in machine learning, econometrics, and the evolution of cultural systems.

Christopher Sweeney
Masters student at MIT Computer Science and Electrical Engineering, specializing in machine learning.

Olasubomi O Olubeko
Masters student at MIT Computer Science and Electrical Engineering, specializing in machine learning.