Explore the crucial role of robustness in AI ethics when defending against adversarial attacks that manipulate data to deceive AI models, as demonstrated in the context of medical x-ray analysis.
Table of Contents
Question
The first x-ray in the following graphic shows no disease. An adversary adds noise to the data and then sends the data through the AI model, which results in an x-ray showing disease present.
Which of the following areas of AI ethics is involved in defending against such an attack?
A. Fairness
B. Transparency
C. Explainability
D. Robustness
E. Privacy
Answer
D. Robustness
Explanation
Adversarial robustness refers to an AI model’s ability to resist being fooled. Safeguarding against attacks involves making AI systems more impervious to attacks and irregularities.
The area of AI ethics involved in defending against such an attack, where noise is added to an x-ray resulting in a false indication of disease, is robustness.
Robustness in AI ethics refers to the strength and stability of an AI system in the face of adversarial inputs or conditions. It ensures that the system can handle and resist attempts to manipulate its outputs, maintaining its integrity and reliability.
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