Survey on AI Ethics: A Socio-technical Perspective

AI ethics requires a "socio-technical" approach: The paper emphasizes that ethical AI concerns cannot be solved through technical solutions alone. Each major ethical principle—fairness, privacy, accountability, safety, transparency, and environmental impact—must be addressed by considering both technical implementations and their social consequences, involving all stakeholders in the AI system lifecycle.

Six foundational ethical principles guide responsible AI deployment: The paper identifies fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact as the core ethical considerations. These principles apply across the entire lifecycle of AI systems, from development through deployment.

Safety-critical applications demand urgent ethical attention: As deep learning models are increasingly deployed in high-stakes scenarios where decisions have significant real-world consequences, the ethical implications have become "highly relevant and essential." This urgency has prompted governments to establish guidelines for ethical AI deployment.

Multiple stakeholders must be engaged in ethical AI governance: The paper stresses that ethical concerns "concern every stakeholder in the AI system lifecycle," suggesting that responsibility for ethical AI cannot fall on developers alone but requires involvement from policymakers, users, affected communities, and other parties throughout the system's life.

Current ethical concerns must be balanced against future risks: The paper acknowledges both present ethical challenges and longer-term existential risks ("x-risk concerns"), indicating that responsible AI governance requires addressing immediate fairness and safety issues while also planning for potential future risks.