Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 28th International Joint Conference on Artificial Intelligence |
| Subtitle of host publication | IJCAI 2019 |
| Editors | Sarit Kraus |
| Pages | 1480-1486 |
| Number of pages | 7 |
| ISBN (electronic) | 9780999241141 |
| Publication status | Published - 2019 |
| Event | 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China Duration: 10 Aug 2019 → 16 Aug 2019 |
Publication series
| Name | Proceedings of the International Joint Conference on Artificial Intelligence |
|---|---|
| ISSN (electronic) | 1045-0823 |
Abstract
Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well as offline services. These systems, depend on sophisticated learning algorithms and available data, to optimize the service function for decision support assistance. However, there is a growing concern about the accountability and fairness of the employed models by the fact that often the available historic data is intrinsically discriminatory, i.e., the proportion of members sharing one or more sensitive attributes is higher than the proportion in the population as a whole when receiving positive classification, which leads to a lack of fairness in decision support system. A number of fairness-aware learning methods have been proposed to handle this concern. However, these methods tackle fairness as a static problem and do not take the evolution of the underlying stream population into consideration. In this paper, we introduce a learning mechanism to design a fair classifier for online stream based decision-making. Our learning model, FAHT (Fairness-Aware Hoeffding Tree), is an extension of the well-known Hoeffding Tree algorithm for decision tree induction over streams, that also accounts for fairness. Our experiments show that our algorithm is able to deal with discrimination in streaming environments, while maintaining a moderate predictive performance over the stream.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
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Proceedings of the 28th International Joint Conference on Artificial Intelligence: IJCAI 2019. ed. / Sarit Kraus. 2019. p. 1480-1486 (Proceedings of the International Joint Conference on Artificial Intelligence).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - FAHT
T2 - 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
AU - Zhang, Wenbin
AU - Ntoutsi, Eirini
PY - 2019
Y1 - 2019
N2 - Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well as offline services. These systems, depend on sophisticated learning algorithms and available data, to optimize the service function for decision support assistance. However, there is a growing concern about the accountability and fairness of the employed models by the fact that often the available historic data is intrinsically discriminatory, i.e., the proportion of members sharing one or more sensitive attributes is higher than the proportion in the population as a whole when receiving positive classification, which leads to a lack of fairness in decision support system. A number of fairness-aware learning methods have been proposed to handle this concern. However, these methods tackle fairness as a static problem and do not take the evolution of the underlying stream population into consideration. In this paper, we introduce a learning mechanism to design a fair classifier for online stream based decision-making. Our learning model, FAHT (Fairness-Aware Hoeffding Tree), is an extension of the well-known Hoeffding Tree algorithm for decision tree induction over streams, that also accounts for fairness. Our experiments show that our algorithm is able to deal with discrimination in streaming environments, while maintaining a moderate predictive performance over the stream.
AB - Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well as offline services. These systems, depend on sophisticated learning algorithms and available data, to optimize the service function for decision support assistance. However, there is a growing concern about the accountability and fairness of the employed models by the fact that often the available historic data is intrinsically discriminatory, i.e., the proportion of members sharing one or more sensitive attributes is higher than the proportion in the population as a whole when receiving positive classification, which leads to a lack of fairness in decision support system. A number of fairness-aware learning methods have been proposed to handle this concern. However, these methods tackle fairness as a static problem and do not take the evolution of the underlying stream population into consideration. In this paper, we introduce a learning mechanism to design a fair classifier for online stream based decision-making. Our learning model, FAHT (Fairness-Aware Hoeffding Tree), is an extension of the well-known Hoeffding Tree algorithm for decision tree induction over streams, that also accounts for fairness. Our experiments show that our algorithm is able to deal with discrimination in streaming environments, while maintaining a moderate predictive performance over the stream.
UR - http://www.scopus.com/inward/record.url?scp=85074920231&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2019/205
DO - 10.24963/ijcai.2019/205
M3 - Conference contribution
AN - SCOPUS:85074920231
T3 - Proceedings of the International Joint Conference on Artificial Intelligence
SP - 1480
EP - 1486
BT - Proceedings of the 28th International Joint Conference on Artificial Intelligence
A2 - Kraus, Sarit
Y2 - 10 August 2019 through 16 August 2019
ER -