Ukkonen, T. (2016). How to prevent discrimination of machine learning models against variables such as age or gender etc. ?. PHILICA.COM Observation number 135.
How to prevent discrimination of machine learning models against variables such as age or gender etc. ?

Tomas Ukkonenunconfirmed user (Helsinki University of Technology)

Published in matho.philica.com

Observation
It is possible to integrate over nuisance variables n:

Integral p(loan, n | input) dn =
Integral p(loan | n, input) p(n | input) dn

And it is possible to model machine learning by modelling them as probability distributions. For example, in case of gender one can create specific models for each gender and calculate:

p(loan | input) =

p(loan | M, input) p(M | input) + p(loan | F, input) p(F | input)

Information about this Observation
This Observation has not yet been peer-reviewed

Published on Wednesday 12th October, 2016 at 17:58:14.

Creative Commons License
This work is licensed under a Creative Commons Attribution 2.5 License.
The full citation for this Observation is:
Ukkonen, T. (2016). How to prevent discrimination of machine learning models against variables such as age or gender etc. ?. PHILICA.COM Observation number 135.




Website copyright © 2006-07 Philica; authors retain the rights to their work under this Creative Commons License and reviews are copyleft under the GNU free documentation license.
Using this site indicates acceptance of our Terms and Conditions.

This page was generated in 0.0084 seconds.