Ethical Boundaries in Human-AI Collaborative Decision Systems: A Review

Abstract

This review covers human–artificial intelligence collaboration in making decisions, observing how scholarship has moved from thinking of AI as a passive tool to considering it as an active actor in healthcare, management, and ethics. While AI systems provide for efficient data processing and pattern recognition, humans provide contextual understanding, ethical judgment, creativity, and accountability. Previous literature suggests that well-designed collaboration between humans and AI may outperform completely automated decision-making. However, several key challenges persist, like bias, lack of transparency, miscalibrated trust, poorly defined distribution of roles, and accountability gaps. To handle these concerns, human-centered design, explainability in AI, well-articulated guidelines on ethical consideration, and mechanisms that effectively introduce oversight remain at the core of literature. These approaches jointly try to ensure that human–AI decision-making systems are fair, accountable, and trustworthy in high-stakes and critical settings