FairWare 2018 http://fairware.cs.umass.edu Welcome from the - - PowerPoint PPT Presentation

fairware 2018
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FairWare 2018 http://fairware.cs.umass.edu Welcome from the - - PowerPoint PPT Presentation

FairWare 2018 http://fairware.cs.umass.edu Welcome from the organizers Brittany Johnson Alexandra Meliou Yuriy Brun http://fairware.cs.umass.edu FairWare 2018 Schedule http://fairware.cs.umass.edu FairWare 2018 Schedule


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FairWare 2018

http://fairware.cs.umass.edu

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Welcome from the organizers

http://fairware.cs.umass.edu

Yuriy Brun Alexandra Meliou Brittany Johnson

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FairWare 2018 Schedule

http://fairware.cs.umass.edu

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FairWare 2018 Schedule

http://fairware.cs.umass.edu

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Keynotes

http://fairware.cs.umass.edu

Aws Albarghouthi

University of Wisconsin-Madison

Ricardo Silva

University College London

Julia Stoyanovich

Drexel University

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Software can make bad decisions. Software can discriminate!

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SLIDE 7
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SLIDE 8

Rachael Tatman, "Gender and Dialect Bias in YouTube's Automatic Captions" in 2017 Workshop on Ethics in Natural Language Processing

YouTube Automatic captions

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Rachael Tatman, "Gender and Dialect Bias in YouTube's Automatic Captions" in 2017 Workshop on Ethics in Natural Language Processing

YouTube Automatic captions

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Joy Buolamwini 


https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms

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fairness in machine learning

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systems work in fairness

  • Aws Albarghouthi, Loris D'Antoni, Samuel Drews, and Aditya Nori, 


FairSquare: Probabilistic Verification for Program Fairness, in OOPSLA 2017
 https://doi.org/10.1145/3133904 
 http://pages.cs.wisc.edu/~aws/papers/oopsla17.pdf

  • Julia Stoyanovich, Ke Yang, and HV Jagadish, 


Online Set Selection with Fairness and Diversity Constraints, in EDBT 2018
 http://dx.doi.org/10.5441/002/edbt.2018.22 
 https://openproceedings.org/2018/conf/edbt/paper-98.pdf

  • Florian Tramer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Jean-Pierre Hubaux, 


Mathias Humbert, Ari Juels, and Huang Lin, 
 FairTest: Discovering Unwarranted Associations in Data-Driven Applications, in EuroS&P 2017.
 https://doi.org/10.1109/EuroSP.2017.29
 https://www.youtube.com/watch?v=IZIpbXtDYT4

  • Sainyam Galhotra, Yuriy Brun, and Alexandra Meliou, 


Fairness Testing: Testing Software for Discrimination, in ESEC/FSE 2017. 
 http://dx.doi.org/10.1145/3106237.3106277
 https://tinyurl.com/FairnessPaper
 https://tinyurl.com/FairnessVideo

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SLIDE 13

systems problems

  • Specifying fairness requirements
  • Generating tests to verify fairness
  • Validating and verifying fairness
  • Maintaining fairness
  • … and all other aspects of the software


engineering lifecycle

  • h, and transparency, accountability, and explainability too!
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FairWare goals

  • Cutting edge systems work
  • Connect with ML, policy, etc. research
  • Identify challenges and research directions
  • Enable collaborations
  • Discuss standards