| Item Type |
Conference or Workshop Item
(Paper)
|
| Abstract |
Machine learning (ML)-based software’s deployment has raised serious concerns about its pervasive and harmful consequences for users, business, and society inflicted through bias. While approaches to address bias are increasingly recognized and developed, our understanding of debiasing remains nascent. Research has yet to provide a comprehensive coverage of this vast growing field, much of which is not embedded in theoretical understanding. Conceptualizing and structuring the nature, effect, and implementation of debiasing instruments could provide necessary guidance for practitioners investing in debiasing efforts. We develop a taxonomy that classifies debiasing instrument characteristics into seven key dimensions. We evaluate and refine our taxonomy through nine experts and apply our taxonomy to three actual debiasing instruments, drawing lessons for the design and choice of appropriate instruments. Bridging the gaps between our conceptual understanding of debiasing for ML-based software and its organizational implementation, we discuss contributions and future research. |
| Authors |
Schmitt, Anuschka; Walser, Maximilian & Fahse, Tobias Benjamin |
| Research Team |
IWI6 |
| Journal or Publication Title |
International Conference of Information Systems (ICIS) |
| Language |
English |
| Keywords |
algorithmic bias, cognitive bias, debiasing, machine learning |
| Subjects |
information management |
| HSG Classification |
contribution to scientific community |
| Date |
12 December 2022 |
| Place of Publication |
Copenhagen, Denmark |
| Number of Pages |
18 |
| Event Title |
International Conference of Information Systems (ICIS) |
| Event Location |
Copenhagen, Denmark |
| Event Dates |
9-14 Dec 2022 |
| Depositing User |
Dr. Mahei Li
|
| Date Deposited |
24 Nov 2022 14:27 |
| Last Modified |
15 Dec 2022 16:25 |
| URI: |
https://www.alexandria.unisg.ch/publications/268106 |
Conceptual Foundations on Debiasing for Machine Learning-Based Software
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