Towards equitable AI in HR: designing a fair, reliable, and transparent human resource management application
- The aim of this work is the development of artificial intelligence (AI) application to support the recruiting process that elevates the domain of human resource management by advancing its capabilities and effectiveness. This affects recruiting processes and includes solutions for active sourcing, i.e. active recruitment, pre-sorting, evaluating structured video interviews and discovering internal training potential. This work highlights four novel approaches to ethical machine learning. The first is precise machine learning for ethically relevant properties in image recognition, which focuses on accurately detecting and analysing these properties. The second is the detection of bias in training data, allowing for the identification and removal of distortions that could skew results. The third is minimising bias, which involves actively working to reduce bias in machine learning models. Finally, an unsupervised architecture is introduced that can learn fair results even without ground truth data. Together, these approaches represent important steps forward in creating ethical and unbiased machine learning systems.
Author of HS Reutlingen | Danner, Michael; Hadžić, Bakir; Weber, Thomas; Rätsch, Matthias |
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DOI: | https://doi.org/10.1007/978-3-031-39059-3_21 |
ISBN: | 978-3-031-39058-6 |
ISBN: | 978-3-031-39059-3 |
Erschienen in: | Deep Learning Theory and Applications : 4th International Conference, DeLTA 2023, Rome, Italy, 13-14 July 2023, proceedings |
Publisher: | Springer |
Place of publication: | Cham |
Editor: | Donatello Conte, Ana Fred, Oleg Gusikhin, Carlo Sansone |
Document Type: | Conference proceeding |
Language: | English |
Publication year: | 2023 |
Page Number: | 18 |
First Page: | 308 |
Last Page: | 325 |
PPN: | Im Katalog der Hochschule Reutlingen ansehen |
DDC classes: | 004 Informatik |
Open access?: | Nein |
Licence (German): | In Copyright - Urheberrechtlich geschützt |