Data Science Popup Austin: Privilege and Supervised Machine Learning
Watch talk ⇒ http://bit.ly/1SGuwNs I'll use the example of sentiment analysis to show that supervised machine learning has the potential to amplify the voices of the most privileged people in society. A sentiment analysis algorithm is considered ‘table stakes’ for any serious text analytics platform in social media, finance, or security. As an example of supervised machine learning, I'll show how these systems are trained. But I'll also show that they have the unavoidable property that they are better at spotting unsubtle expressions of extreme emotion. Such crude expressions are used by a particularly privileged group of authors: men. In this way, brands that depend on sentiment analysis to 'learn what people think' inevitably pay more attention to men. The problem doesn't stop with sentiment analysis: at every step of any model building process, we make choices that can introduce bias, enhance privilege, or break the law! I'll review these pitfalls, talk about how you can recognize them in your own work, and touch on some new academic work that aims to mitigate these harms.
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Data Science Popup Austin: Privilege and Supervised Machine Learning
- 1. DATA SCIENCE POP UP AUSTIN Privilege and Supervised 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 ethics in 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
- 10. 227ft73ft0ft 293 $/sq ft 1775 $/sq ft 4601 $/sq ft r2d3.us
- 11. supervised machine learning is a formalized method for finding useful rules of thumb
- 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 human beings, supervised machine learning is a formalized method for finding ! useful stereotypes !
- 22. • Civil Rights Acts of 1964 and 1991 • Americans with Disabilities Act • Genetic Information Nondiscrimination Act • Equal Credit Opportunity Act • Fair Housing Act
- 23. disparate treatment ≈ intentional disparate impact ≈ unintentional/implicit
- 24. • automated sentiment systems 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 • Big Data’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