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Suresh Venkatasubramanian
I like algorithms. And I hope they're fair.
I like algorithms. And I hope they're fair.
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The SOCG 2017 call for papers is out. 300-word abstracts are due November 28; papers are due December 5.




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As part of the research we're doing in algorithmic fairness we're looking to hire a post-doctoral researcher who can help us bridge the gap between the more technical aspects of algorithmic fairness and the ways in which this discussion informs and is informed by the larger context in the social science.

+Sorelle Friedler

https://algorithmicfairness.wordpress.com/2016/10/03/post-doc-in-fairness-at-data-and-society/

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21st century living, in a nutshell.

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Moritz tackles a very important issue in this post: defining goals for fairness in Machine Learning. It's trickier than it seems, but a very important topic.
Many have given up on the very concept, often arguing that machine learning isn't biased, but merely mirrors our own biases by fitting models to 'naturally occurring' biased data. That's simply not true: machine learning applied without care is inherently biased toward where there is a lot of data in the first place: think groups of people who consume more, or ethnicities that show up more often on medical records. It is something we can control and characterize however, as long as we have the proper definitions in place of what it means to a probabilistic model to be 'fair'.
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