Discover the crucial pillar of AI ethics that ensures AI models can withstand both intentional and unintentional interference. Learn how robustness is essential for maintaining the integrity and reliability of AI systems in the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
Which of the following pillars of AI ethics is most related to your AI model withstanding intentional and unintentional interference?
A. Fairness
B. Privacy
C. Explainability
D. Transparency
E. Robustness
Answer
The pillar of AI ethics that is most related to an AI model withstanding intentional and unintentional interference is:
E. Robustness
Explanation
Robustness allows AI models to continue to deliver accurate and reliable output even when circumstances change. By protecting AI models from tampering, attacks, and varying conditions, people can have confidence in the AI system’s output.
The pillar of AI ethics most related to an AI model withstanding intentional and unintentional interference is robustness.
Robustness is a critical aspect of AI ethics that focuses on ensuring AI systems are resilient, secure, and can maintain their intended functionality even when faced with adversarial attacks, unexpected inputs, or changes in their operating environment.
Here’s why robustness is the most relevant pillar for this question:
- Intentional interference: Robustness helps AI models defend against malicious attempts to manipulate or deceive the system, such as adversarial attacks designed to fool the model into making incorrect predictions or decisions.
- Unintentional interference: Robust AI models can handle noise, errors, or unexpected variations in input data without significantly impacting their performance or output quality. This is crucial for maintaining the system’s reliability and consistency.
- Model integrity: By focusing on robustness, AI developers can create models that are more stable, trustworthy, and resistant to both internal and external factors that may compromise their integrity or lead to unintended consequences.
- Real-world applicability: As AI systems are increasingly deployed in real-world settings, robustness becomes paramount to ensure they can operate effectively and safely under a wide range of conditions, including those that may not have been anticipated during training.
While other pillars like fairness, privacy, explainability, and transparency are essential for building ethical AI systems, robustness is the most directly related to an AI model’s ability to withstand interference and maintain its intended behavior.
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