Connectionists: ICML Workshop on Fairness, Accountability, and Transparency in Machine Learning

[Sorelle Friedler]

We hope you can join us for this upcoming workshop!

ICML Workshop on Fairness, Accountability, and Transparency in Machine
Learning
Saturday, July 11th, 2015 - Lille, France
www.fatml.org

This interdisciplinary workshop will consider issues of fairness,
accountability, and transparency in machine learning. It will address
growing anxieties about the role that machine learning plays in
consequential decision-making in such areas as commerce, employment,
healthcare, education, and policing.

Invited Speakers:
Nick Diakopoulos --- Algorithmic Accountability and Transparency in
Journalism
Sara Hajian --- Discrimination- and Privacy-Aware Data Mining
Salvatore Ruggieri --- Privacy Attacks and Anonymization Methods as Tools
for Discrimination Discovery and Fairness
Toshihiro Kamishima and Kazuto Fukuchi --- Future Directions of
Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical
Aspects

Accepted Papers:
Muhammad Bilal Zafar, Isabel Valera Martinez, Manuel Gomez Rodriguez, and
Krishna Gummadi --- Fairness Constraints: A Mechanism for Fair
Classification
Benjamin Fish, Jeremy Kun, and Ádám D. Lelkes --- Fair Boosting: A Case
Study
Zubin Jelveh and Michael Luca --- Towards Diagnosing Accuracy Loss in
Discrimination-Aware Classification: An Application to Predictive Policing
Indrė Žliobaitė --- On the Relation between Accuracy and Fairness in Binary
Classification

Closing Panel Discussion:
Fernando Diaz, Sorelle Friedler, Mykola Pechenizkiy, Hanna Wallach, and
Suresh Venkatasubramanian (Moderator)

Looking forward to seeing you in Lille!

The organizing committee,
Solon Barocas (General Chair), Princeton University
Sorelle Friedler (Program Chair), Haverford College
Moritz Hardt, Google
Josh Kroll, Princeton Unviersity
Carlos Scheidegger, University of Arizona
Suresh Venkatasubramanian, University of Utah
Hanna Wallach, Microsoft Research and University of Massachusetts Amherst
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