Learn about the key concept of robustness in AI models and how it enables accurate performance even with noisy or unexpected data, as covered in the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
Bao is reviewing a diagnostic AI model when he notices something strange. The data set is larger than it should be. After reviewing the activity over the past weeks, he notices that extra items were placed in the data set that could have resulted in the AI model rendering incorrect diagnoses. However, the AI model was able to process the bogus data and still render accurate and correct diagnoses for each data item.
This is an example of which of the following?
A. Explainability
B. Robustness
C. Transparency
D. Privacy
E. Fairness
Answer
B. Robustness
Explanation
This scenario illustrates the robustness of the AI model, specifically its ability to handle additional and irrelevant data without compromising the accuracy of its diagnoses.
The scenario described, where the AI diagnostic model is able to accurately process and render correct diagnoses despite being given bogus or incorrect data, is an example of robustness in AI systems.
Robustness refers to the ability of an AI model to maintain its performance and generate accurate outputs even when given noisy, unexpected, or adversarial inputs. A robust model can handle data outside of its training distribution without a significant degradation in performance.
In this case, even though extra incorrect data items were added to the dataset that could have led to wrong diagnoses, the fact that the model still produced accurate results demonstrates its robustness. The model was resilient to the “attacks” of bad data points and able to focus on the relevant features to render the right diagnoses.
The other options do not correctly characterize this scenario:
- Explainability is about understanding how the model makes its decisions, which is not the focus here.
- Transparency relates to openness about the model’s training data, algorithms, and performance, but is not directly illustrated.
- Privacy and fairness are important AI principles but are not the main issue demonstrated by the model’s ability to handle problematic data inputs.
So in summary, robustness, or a model’s resilience to noisy/unexpected/adversarial data, is the key characteristic exemplified here by the diagnostic model’s accurate performance despite the data tampering. Having robust models is important for AI systems to function reliably in real-world settings.
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