Contextualizing Artificially Intelligent Morality: A Meta-Ethnography of Top-Down, Bottom-Up, and Hybrid Models for Theoretical and Applied Ethics in Artificial Intelligence

There are fundamentally different approaches to embedding ethics in AI systems: The paper identifies three distinct models—top-down (ethics imposed by corporations/governments through policy), bottom-up (ethics driven by user needs and community input), and hybrid approaches—each with different strengths and limitations for ensuring responsible AI development.

Top-down policy approaches alone are insufficient: While AI ethics principles represent a practical top-down framework, the paper suggests that relying solely on corporate and governmental mandates to determine ethical standards creates gaps in addressing real-world impacts and user concerns that emerge during deployment.

Technical design choices embed moral values: The paper emphasizes that how AI systems are technically built—using examples like reinforcement learning—directly reflects moral decisions. This means engineers and developers are making ethical choices through their technical implementations, not just through separate ethics committees.

Multiple stakeholder perspectives are necessary: The research highlights that responsible AI requires integrating philosophical ethics, technical considerations, and political/community perspectives. No single angle adequately captures the complexity of AI's expected and unexpected consequences across society.

Global context matters for AI ethics: By examining real-world case studies from different regions, the paper demonstrates that AI ethics cannot be universally standardized; ethical frameworks must account for varying cultural values, political systems, and local impacts to be genuinely responsible.