Ethical AI Principles for Enterprise Collaboration in Federated Learning Networks
Müller K, Kolb L, Lechner U, Bodendorf F (2024)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2024
URI: https://aisel.aisnet.org/ecis2024/track04_impactai/track04_impactai/16/
Abstract
Ethical artificial intelligence (AI) has received increasing attention as the development and use of AI applications have expanded. Collaborative machine learning, such as cross-silo federated learning (FL) can assist in adhering to ethical AI standards. FL ensures privacy and data sovereignty while minimizing model bias by aggregating AI models trained locally on data silos from several organizations. However, decentralization and multi-party involvement necessitate the refinement of existing ethical AI principles and the development or adaptation of methods for compliance. In this study, we conduct a systematic literature review followed by a workshop with participants from academia and industry on ethical principles for FL, considering technical and organizational aspects at different phases of the collaboration cycle. Our contribution is a guideline for technical and non-technical stakeholders to support the ethically aligned establishment/entry, value co-creation, operational continuity, and exit/termination of enterprise FL networks.
Authors with CRIS profile
Kristina Müller
Department of Data Science
Freimut Bodendorf
Lehrstuhl für Wirtschaftsinformatik, insbesondere im Dienstleistungsbereich
Involved external institutions
Germany (DE)How to cite
APA:
Müller, K., Kolb, L., Lechner, U., & Bodendorf, F. (2024). Ethical AI Principles for Enterprise Collaboration in Federated Learning Networks. In Proceedings of the European Conference on Information Systems.
MLA:
Müller, Kristina, et al. "Ethical AI Principles for Enterprise Collaboration in Federated Learning Networks." Proceedings of the European Conference on Information Systems 2024.
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