Paper Type
Complete
Abstract
The rapid evolution of Big Data and Artificial Intelligence has driven innovation and societal progress but also introduces complex ethical and regulatory challenges, particularly concerning data privacy, transparency, and accountability. This paper highlights the need to align technological advancements with legal and ethical frameworks, specifically GDPR and the EU AI Act. Building on Labadie and Legner’s (2023) Capability Model for EU-GDPR, this study refines a Cross-Regulatory Framework to help organizations integrate regulatory requirements into operational principles. The framework enables the design of privacy-preserving technologies while ensuring compliance through regulatory alignment, ethical guidelines, and technical best practices. By synthesizing existing literature and innovative approaches, this study provides a model to ensure ethical and regulatory compliance. Practitioners can leverage this framework to assess data governance and implement systematic compliance strategies for data protection and technique regulations.
Paper Number
1963
Recommended Citation
Yu, Fangzhou; Carton, Fergal; and Xiong, Huanhuan, "A Cross-Regulatory Framework for Data and Technology Management: Addressing Data Protection Regulations and Ethical Considerations" (2025). AMCIS 2025 Proceedings. 23.
https://aisel.aisnet.org/amcis2025/data_science/sig_dsa/23
A Cross-Regulatory Framework for Data and Technology Management: Addressing Data Protection Regulations and Ethical Considerations
The rapid evolution of Big Data and Artificial Intelligence has driven innovation and societal progress but also introduces complex ethical and regulatory challenges, particularly concerning data privacy, transparency, and accountability. This paper highlights the need to align technological advancements with legal and ethical frameworks, specifically GDPR and the EU AI Act. Building on Labadie and Legner’s (2023) Capability Model for EU-GDPR, this study refines a Cross-Regulatory Framework to help organizations integrate regulatory requirements into operational principles. The framework enables the design of privacy-preserving technologies while ensuring compliance through regulatory alignment, ethical guidelines, and technical best practices. By synthesizing existing literature and innovative approaches, this study provides a model to ensure ethical and regulatory compliance. Practitioners can leverage this framework to assess data governance and implement systematic compliance strategies for data protection and technique regulations.
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