loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Wenlong Sun 1 ; Olfa Nasraoui 1 and Patrick Shafto 2

Affiliations: 1 Dept of Computer Engineering and Computer Science, University of Louisville, Louisville, KY and U.S.A. ; 2 Dept of Mathematics and Computer Science, Rutgers University - Newark, Newark, NJ and U.S.A.

Keyword(s): Information Retrieval, Machine Learning, Bias, Iterative Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems ; User Profiling and Recommender Systems

Abstract: Early supervised machine learning (ML) algorithms have used reliable labels from experts to build predictions. But recently, these algorithms have been increasingly receiving data from the general population in the form of labels, annotations, etc. The result is that algorithms are subject to bias that is born from ingesting unchecked information, such as biased samples and biased labels. Furthermore, people and algorithms are increasingly engaged in interactive processes wherein neither the human nor the algorithms receive unbiased data. Algorithms can also make biased predictions, known as algorithmic bias. We investigate three forms of iterated algorithmic bias and how they affect the performance of machine learning algorithms. Using controlled experiments on synthetic data, we found that the three different iterated bias modes do affect the models learned by ML algorithms. We also found that Iterated filter bias, which is prominent in personalized user interfaces, can limit human s’ ability to discover relevant data. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.239.185.22

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Sun, W.; Nasraoui, O. and Shafto, P. (2018). Iterated Algorithmic Bias in the Interactive Machine Learning Process of Information Filtering. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KDIR; ISBN 978-989-758-330-8; ISSN 2184-3228, SciTePress, pages 110-118. DOI: 10.5220/0006938301100118

@conference{kdir18,
author={Wenlong Sun. and Olfa Nasraoui. and Patrick Shafto.},
title={Iterated Algorithmic Bias in the Interactive Machine Learning Process of Information Filtering},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KDIR},
year={2018},
pages={110-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006938301100118},
isbn={978-989-758-330-8},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KDIR
TI - Iterated Algorithmic Bias in the Interactive Machine Learning Process of Information Filtering
SN - 978-989-758-330-8
IS - 2184-3228
AU - Sun, W.
AU - Nasraoui, O.
AU - Shafto, P.
PY - 2018
SP - 110
EP - 118
DO - 10.5220/0006938301100118
PB - SciTePress