Image from “The Library of Missing Datasets” by Mimi Onuoha (2016)
As algorithmic decisions and likelihood predictions reach ever more deeply, and with increasing consequence, into our lives, there is an increasing mandate that they be “fair”.
Who counts? Machine learning algorithms learn from training data; when these are biased so are the algorithms they produce. The workshop will examine sources of sex and gender bias in data, with emphases on impoverished women; women of color; trans and non-binary persons; and older women.
This program is preceded by a Graduate Summer School on Algorithmic Fairness organized by Cynthia Dwork, and Guy Rothblum (Weizmann Institute of Science).
Ruha Benjamin
(Princeton University)
Cynthia Dwork
(Harvard University)
Patricia Williams
(Northeastern University)
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