Trustworthiness and Reliability of AI

YONG : compare "Trustworthiness" and "Reliablility" of AI

 

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Trustworthiness and reliability are two important concepts in the field of artificial intelligence (AI). They are often used interchangeably, but there are some key differences between the two.

• Trustworthiness is the degree to which a person or system is believed to be honest, reliable, and fair. It is based on a number of factors, including the system's accuracy, performance, fairness, transparency, and accountability.
• Reliability is the degree to which a system can be depended on to perform its functions correctly and consistently. It is based on the system's accuracy, robustness, and robustness to failure.

In general, a trustworthy AI system is also reliable, but a reliable AI system may not be trustworthy. For example, an AI system that is very accurate and performs its functions consistently may not be trustworthy if it is not transparent or accountable.

Here is a table that summarizes the key differences between trustworthiness and reliability:

FeatureTrustworthinessReliability

Definition	The degree to which a person or system is believed to be honest, reliable, and fair.	The degree to which a system can be depended on to perform its functions correctly and consistently.
Factors	Accuracy, performance, fairness, transparency, accountability	Accuracy, robustness, robustness to failure
Relationship	Trustworthiness implies reliability, but reliability does not imply trustworthiness.

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It is important to consider both trustworthiness and reliability when evaluating AI systems. Trustworthiness is important because it allows users to feel confident that the system will make accurate and fair decisions. Reliability is important because it ensures that the system will be available when needed and will not fail catastrophically.

Here are some examples of how trustworthiness and reliability are important in AI:

• In healthcare, AI systems are being used to make diagnoses and recommend treatments. It is important for these systems to be trustworthy so that patients can feel confident in their recommendations.
• In finance, AI systems are being used to make investment decisions. It is important for these systems to be reliable so that investors can trust that their money is safe.
• In transportation, AI systems are being used to control autonomous vehicles. It is important for these systems to be both trustworthy and reliable so that they can safely transport people and goods.

As AI systems become more complex and powerful, it will become increasingly important to ensure that they are both trustworthy and reliable. This is a challenge that the AI community is working hard to address.