Don’t pretend algorithms are "objective"

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References for Supporting diversity with a new approach to software

Open Source Bridge sessions

The Consequences of an Insightful Algorithm, Carina C. Zona, Open Source Bridge 2015

Links

Big Risks, Big Opportunities: the Intersection of Big Data and Civil Rights: The latest White House report on Big Data charts pathways for fairness and opportunity but also cautions against re-encoding bias and discrimination into algorithmic systems. "The algorithmic systems that turn data into information are not infallible—they rely on the imperfect inputs, logic, probability, and people who design them."

Machine Bias, Julia Angwin et. al., Pro Publica. "There’s software used across the country to predict future criminals. And it’s biased against blacks."

What does it mean for an algorithm to be fair?, Jeremy Kun

Big Data, Machine Learning, and the Social Sciences: Fairness, Accountability, and Transparency. Hanna Wallach

Critical Algorithm Studies: a Reading List, from the Social Media Collective at Microsoft: the literature on algorithms as social processes.

Fairness in Machine learning, a slide deck from Delip Rao, includes a short reading list

Three Black Teenagers Google search sparks outrage, USA Today

Can computers be racist? Big data, inequality, and discrimination, Ford Foundation, based on a series of presentations by Latanya Sweeney and Alvaro Bedoya.

Discrimination and Opacity in Online Behavioral Advertising, Datta et. al, 'Proceedings on Privacy Enhancing Technologies, 2015

Google’s autocompletion: algorithms, stereotypes, and accountability, Anna Jobin, Postcolonial Digital Humanities