Don’t pretend algorithms are "objective"
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
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."
Pro Publica's Machine Bias series, by Julia Angwin et. al., was a Pulitzer Prize finalist. The first article in the series Machine Bias has a pithy summary: "There’s software used across the country to predict future criminals. And it’s biased against blacks."
Artificial intelligence: How to avoid racist algorithms, by Zoe Kleinman on BBC News, is a good overview, with perspectives from World White Web designer Johanna Burai, Algorithmic Justice League (AJL) creator Joy Buolamwini, and Suresh Venkatasubramanian of the University of Utah.
What does it mean for an algorithm to be fair?, Jeremy Kun
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
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