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Data Science Popup Austin: Privilege and Supervised Machine Learning
Mike Williams discusses the impact of bias and ethics in supervised machine learning, particularly in sentiment analysis on social media. He highlights how algorithms can amplify men's voices and privilege by focusing on clear expressions of emotion, thereby recapitulating historical biases in training data. The talk emphasizes the need to consider ethical, legal, and commercial consequences when building such models.
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Data Science Popup Austin: Privilege and Supervised Machine Learning
- 1. DATA SCIENCE POP UP AUSTIN Privilege andSupervised Machine Learning Mike Williams Research Engineer, Fast Forward Labs mikepqr
- 2. DATA SCIENCE POP UP AUSTIN #datapopupaustin April 13,2016 Galvanize, Austin Campus
- 4. Bias and ethicsin supervised machine learning Mike Williams @mikepqr, mike.place/talks Fast Forward Labs, fastforwardlabs.com github.com/williamsmj/sentiment
- 6. a specific example:sentiment analysis on social media more general points about supervised machine learning
- 9.
- 10. 227ft73ft0ft 293 $/sq ft1775 $/sq ft 4601 $/sq ft r2d3.us
- 11. supervised machine learning isa formalized method for finding useful rules of thumb
- 12.
- 13. what’s the problem? •our system attempts to algorithmically identify ‘negative sentiment’ • it does a better job of finding strident, unambiguous expressions of emotion • men are more likely to make such expressions • men therefore attract disproportionately more attention from brands using these products • we amplified their privilege
- 15. a specific example:sentiment analysis on social media more general points about supervised machine learning
- 16. applied to humanbeings, supervised machine learning is a formalized method for finding ! useful stereotypes !
- 20.
- 22. • Civil RightsActs of 1964 and 1991 • Americans with Disabilities Act • Genetic Information Nondiscrimination Act • Equal Credit Opportunity Act • Fair Housing Act
- 23. disparate treatment ≈ intentional disparateimpact ≈ unintentional/implicit
- 24. • automated sentimentsystems are used to ‘learn what people think’ on social media • in practice they amplify the voices of men, enhancing their privilege • applied to humans, supervised machine learning is an attempt to discover which stereotypes are true • when you build a model, consider the ethical, legal and commercial consequences of the choices you make
- 25. Follow up • BigData’s Disparate Impact, Barocas and Selbst, 2016, California Law Review • Solon Barocas, Moritz Hardt, Cathy O’Neill, Delip Rao, Sorelle Friedler • mike@mike.place, @mikepqr, github.com/ williamsmj/sentiment, fastforwardlabs.com
- 26.