What ethical principle requires that AI decision-making systems be understandable and open to examination by affected individuals?

Question

Accepted Answer

Transparency. Transparency in AI ethics requires that automated decision-making systems be understandable, explainable, and open to scrutiny by the people they affect. This principle holds that individuals have a right to know when AI is being used to make decisions about them and to understand the general logic behind those decisions. Transparency is essential for accountability - without it, there is no way to verify that an AI system is operating fairly, correctly, and in accordance with applicable laws and ethical standards.

Scalability refers to a system's ability to handle increasing amounts of work or users efficiently, which is a technical performance characteristic rather than an ethical principle governing how AI decisions are communicated to affected parties. Profitability is a business metric measuring financial returns, which has no inherent connection to whether AI systems are explainable or open to examination. Backward compatibility is a software engineering concept describing whether newer systems can work with older ones, which addresses technical interoperability rather than the ethical imperative of making AI decisions understandable.

Transparency is one of the most important principles in AI ethics and is frequently tested on the Ethics in Technology exam. The "black box" problem - where complex machine learning models make decisions that even their creators cannot fully explain - poses significant challenges for transparency. Students should understand how transparency relates to accountability, fairness, and trust, as well as regulatory efforts like the GDPR's "right to explanation" and proposals for AI audit requirements.

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