Na delavnici ICML 2015, 11. julija, Lille, Francija, o strojnem učenju (“machine learinig”) bodo obravnavali ključno tematiko velikega podatkovja in algoritmičnih predikcij: 

kako preprečiti vkodiranje predsodkov v avtomatizirane odločitve?

Pravni odločevalci se bodo s sistemi za pomoč pri odločanju, ki temeljijo na algoritmih, (lahko) hitro skrili za algoritem, češ “the algorithm made me do it”. Vprašanje odgovornosti za odločitve je eno izmed ključnih pri uporabi velikega podatkovja za odločitve.

Submission Deadline: May 1, 2015.


We welcome contributions on theoretical models, empirical work, and everything in between, including (but not limited to) contributions that address the following open questions:

* How can we achieve high classification accuracy while preventing discriminatory biases?

* What are meaningful formal fairness properties?

* What is the best way to represent how a classifier or model has generated a particular result?

* Can we certify that some output has an explanatory representation?

* How do we balance the need for knowledge of sensitive attributes for  fair modeling and classification with concerns and limitations around the collection and use of sensitive attributes?

* What ethical obligations does the machine learning community have when models affect the lives of real people?


Papers are limited to four content pages, including figures and tables, and must follow the ICML 2015 format; however, an additional fifth page containing only cited references is permitted. Papers SHOULD be anonymized. Accepted papers will be made available on the workshop website; however, the workshop’s proceedings can be considered non-archival, meaning contributors are free to publish their work in archival journals or conferences. Accepted papers will be either presented as a talk or poster (to be determined by the workshop organizers). 

Papers should be submitted here:

Deadline for submissions: May 1, 2015
Notification of acceptance: May 10, 2015


Workshop Organizers:

Solon Barocas, Princeton University
Sorelle Friedler, Haverford College
Moritz Hardt, IBM Almaden Research Center
Joshua Kroll, Princeton University
Carlos Scheidegger, University of Arizona
Suresh Venkatasubramanian, University of Utah
Hanna Wallach, Microsoft Research NYC