Which principle addresses the need for AI accountability in decision-making?

The principle that addresses the need for AI accountability in decision-making is accountability itself. This principle emphasizes the importance of ensuring that there are clear lines of responsibility for AI systems and their outcomes. It involves establishing mechanisms to hold individuals or organizations responsible for the actions and decisions made by AI systems.

In the context of AI, accountability ensures that stakeholders can be held responsible if an AI system makes erroneous or biased decisions. This includes creating processes for monitoring, auditing, and correcting AI decisions to build trust and ensure adherence to ethical standards. It also involves the ability to provide explanations when an AI system affects individuals or communities, thereby reinforcing the importance of oversight and governance in AI applications.

Inclusiveness is related to ensuring diverse perspectives in AI development but does not specifically focus on accountability. Fairness pertains to preventing biased outcomes rather than establishing accountability. Transparency involves making processes and data understandable to help in accountability but does not itself define accountability in decision-making.