Jaspreet Sahota, The Problem of Historical Bias in Supervised Machine Learn...

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Centre for Ethics

Room 200, Second Floor, Larkin Building, 15 Devonshire Place

Toronto, ON, Ontario M5S 1H8

Canada

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The Problem of Historical Bias in Supervised Machine Learning

Machine learning algorithms are becoming ubiquitous in business and
government. Algorithms are routinely deployed that make decisions
about how we live: e.g. credit adjudication, parole approval, resume
screening, insurance costs, etc. Training supervised algorithms on the
basis of historical data has the risk of perpetuating historical
biases in contemporary society. This can lead to a pernicious feedback
cycle that should be avoided by eliminating bias from training data
and furthering research into deep learning models.

Jaspreet Sahota
Independent Researcher
Ph.D. Physics, University of Toronto

04:00 PM - 05:30 PM
Centre for Ethics, University of Toronto
200 Larkin

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Centre for Ethics

Room 200, Second Floor, Larkin Building, 15 Devonshire Place

Toronto, ON, Ontario M5S 1H8

Canada

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