Machine learning

Abstract

This dissertation explores the integration of machine learning (ML) and artificial intelligence (AI) within organizational information systems, focusing on their dual role as technological enablers and sources of governance challenges. The research addresses the societal and organizational implications of algorithmic decision-making (ADM). ML-enabled ADM is explored from two main perspectives: 1) ethical dimension, particularly on fairness, discrimination, and privacy, and 2) value creation from ML in organizational settings. In summary, this work provides a comprehensive analysis of the challenges and solutions for deploying responsible AI and ML in organizations, emphasizing the need to balance fairness, privacy, and operational effectiveness.