Flexible and Adaptive Fairness-aware Learning in Non-stationary Data Streams
Paper
Paper/Presentation Title | Flexible and Adaptive Fairness-aware Learning in Non-stationary Data Streams |
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Presentation Type | Paper |
Authors | Zhang, Wenbin (Author), Zhang, Mingli (Author), Zhang, Ji (Author), Liu, Zhen (Author), Chen, Zhiyuan (Author), Wang, Jianwu (Author), Raff, Edward (Author) and Messina, Enza (Author) |
Editors | Alamaniotis, Miltos and Pan, Shimei |
Journal or Proceedings Title | Proceedings IEEE 32nd International Conference on Tools with Artificial Intelligence |
ERA Conference ID | 43564 |
Number of Pages | 8 |
Year | 2020 |
Place of Publication | Piscataway, United States |
ISBN | 9781728192284 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICTAI50040.2020.00069 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/9288346 |
Conference/Event | 32nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2020) |
International Conference on Tools with Artificial Intelligence | |
Event Details | International Conference on Tools with Artificial Intelligence ICTAI Rank B B B B B B B |
Event Details | 32nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2020) Event Date 09 to end of 11 Nov 2020 Event Location Baltimore, United States |
Abstract | Artificial intelligence (AI)-based decision-making systems are employed nowadays in an ever growing number of online as well as offline services-some of great importance. Depending on sophisticated learning algorithms and available data, these systems are increasingly becoming automated and data-driven. However, these systems can impact individuals and communities with ethical or legal consequences. Numerous approaches have therefore been proposed to develop decision-making systems that are discrimination-conscious by-design. However, these methods assume the underlying data distribution is stationary without drift, which is counterfactual in many realworld applications. In addition, their focus has been largely on minimizing discrimination while maximizing prediction performance without necessary flexibility in customizing the tradeoff according to different applications. To this end, we propose a learning algorithm for fair classification that also adapts to evolving data streams and further allows for a flexible control on the degree of accuracy and fairness. The positive results on a set of discriminated and non-stationary data streams demonstrate the effectiveness and flexibility of this approach. |
Keywords | AI fairness, online classification, flexible fairness |
ANZSRC Field of Research 2020 | 460501. Data engineering and data science |
Byline Affiliations | University of Maryland, United States |
McGill University, Canada | |
University of Southern Queensland | |
Guangdong Pharmaceutical University, China | |
Booz Allen Hamilton, United States | |
University of Milano-Bicocca, Italy | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q63wq/flexible-and-adaptive-fairness-aware-learning-in-non-stationary-data-streams
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