Prediction with Model-Based Neutrality
-
- FUKUCHI Kazuto
- Graduate School of System and Information Engineering, University of Tsukuba
-
- KAMISHIMA Toshihiro
- National Institute of Advanced Industrial Science and Technology (AIST)
-
- SAKUMA Jun
- Graduate School of System and Information Engineering, University of Tsukuba
Search this article
Abstract
With recent developments in machine learning technology, the predictions by systems incorporating machine learning can now have a significant impact on the lives and activities of individuals. In some cases, predictions made by machine learning can result unexpectedly in unfair treatments to individuals. For example, if the results are highly dependent on personal attributes, such as gender or ethnicity, hiring decisions might be discriminatory. This paper investigates the neutralization of a probabilistic model with respect to another probabilistic model, referred to as a viewpoint. We present a novel definition of neutrality for probabilistic models, η-neutrality, and introduce a systematic method that uses the maximum likelihood estimation to enforce the neutrality of a prediction model. Our method can be applied to various machine learning algorithms, as demonstrated by η-neutral logistic regression and η-neutral linear regression.
Journal
-
- IEICE Transactions on Information and Systems
-
IEICE Transactions on Information and Systems E98.D (8), 1503-1516, 2015
The Institute of Electronics, Information and Communication Engineers
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390001204377740800
-
- NII Article ID
- 130005090405
-
- NII Book ID
- AA10826272
-
- ISSN
- 17451361
- 09168532
-
- HANDLE
- 2241/00128648
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- IRDB
- Crossref
- CiNii Articles
- KAKEN
-
- Abstract License Flag
- Disallowed