Dr. Ramon Amaro

Dr. Ramon Amaro is an engineer, sociologist, and cultural theorist in Machine Learning and Artificial Intelligence research. He provides fundamental research on race, ethics and responsible AI/ML, including the societal implications of data and digital technologies.

As an interdisciplinary engineer and social scientist, Amaro brings a unique perspective to human-technical research, as he understands the deep technical details of machine learning, as well as the study of human and social behavior. His research can then be used by scientists and engineers to make improvements on existing products, technologies and processes.

Amaro has a unique set of core qualifications, which include a Ph.D. in Philosophy (of Technology) from Goldsmiths, University of London, where he studied the philosophy of machine learning and the history of racial bias in mathematics. He also holds a Master of Arts Degree in Sociological Research from the University of Essex, where he analyzed the impact of racial and ethnic origin on individual decision-making and group interaction. Lastly, Amaro has a Bachelors of Science in Mechanical Engineering from the University of Michigan, Ann Arbor, where he specialized in the principles and practice of product and service quality assurance and control.

Amaro is an experienced academic as well as industry researcher in digital ethics with a diverse delivery portfolio. Prior to his present role as Assistant Professor at University College London (UCL), Amaro worked as a Quality Engineer for General Motors Corp.; Engineering Programs Manager in Renewable Energies for the American Society of Mechanical Engineers (ASME), and as an Assistant Professor in Visual Cultures at Goldsmiths, University of London. His expertise expands across a range of subject areas, from theory to practical analysis.

These skills are brought together in Amaro's new book "The Black Technical Object: On Machine Learning and the Aspiration of Black Being” published by Sternberg/MIT Press. This book aims at introducing the history of statistical analysis and a knowledge of sociogenesis — a system of racism amenable to scientific explanation — into machine learning research as an act of impairing the racial ordering of the world.