Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality

Area of Science:

• Computer Science
• Ethics
• Sociology

Background:

• Big Data and Artificial Intelligence (AI) technologies are increasingly prevalent across various sectors.
• Existing literature on AI ethics often lacks empirical grounding in real-world applications.
• Understanding the practical ethical issues faced by organizations is crucial for effective governance.

Purpose of the Study:

• To empirically investigate the ethical use of Big Data and AI (BD+AI) technologies.
• To categorize current literature on BD+AI ethics and compare it with on-the-ground issues.
• To identify and cluster common ethical concerns across diverse organizational domains.

Main Methods:

• Qualitative analysis of ten targeted case studies from various social domains.
• Literature review to categorize existing ethical issues in BD+AI.
• Comparative analysis of identified ethical issues against literature classifications.

Main Results:

• Despite diverse applications, organizations encounter overlapping and correlated ethical concerns regarding BD+AI.
• Empirical findings reveal practical ethical issues that align with, yet offer more granularity than, existing literature.
• A multi-case study approach provided a nuanced understanding of 'on-the-ground' ethical challenges.

Conclusions:

• A deeper, empirically-driven understanding of BD+AI ethics is necessary.
• Targeted mitigation strategies are required to address prevalent ethical issues effectively.
• The study emphasizes the need for practical ethical frameworks tailored to real-world AI implementations.