Discover the significance of transparency in AI models and how it enables users to quickly understand key facts, similar to a nutritional label on food packaging.
Table of Contents
Question
A trustworthy AI model should allow key facts about that model to be seen very quickly, just like the nutritional label on a box of food.
This is an example of which of the following?
A. Robustness
B. Transparency
C. Explainability
D. Fairness
E. Privacy
Answer
B. Transparency
Explanation
People find it hard to trust what they can’t see or understand. By being transparent about how an AI model operates, people can verify what is happening and understand how an AI system is generating its output. Transparency builds trust in AI models.
Transparency is the correct answer to this question. Transparency in AI refers to the practice of making information about an AI model’s development, functionality, and limitations readily available and easily accessible to users and stakeholders.
Just as a nutritional label on a food package provides essential information about the product’s contents, allowing consumers to make informed decisions, a transparent AI model should offer key facts about itself in a clear and concise manner. This transparency enables users to quickly understand the model’s capabilities, limitations, and potential biases.
Transparency is crucial for building trust in AI systems. By being open and honest about how a model was developed, what data it was trained on, and its intended purpose, developers can foster trust among users and demonstrate their commitment to responsible AI practices.
The other options mentioned in the question, while important aspects of AI development, do not directly address the analogy of providing key facts about a model in a readily accessible format:
- Robustness refers to an AI model’s ability to perform well under various conditions and inputs.
- Explainability involves providing insights into how an AI model arrives at its decisions or outputs.
- Fairness ensures that an AI model does not discriminate against certain groups or perpetuate biases.
- Privacy focuses on protecting sensitive information and adhering to data protection regulations.
In summary, transparency is the key principle that aligns with the idea of allowing users to quickly access essential information about an AI model, similar to the way a nutritional label provides clarity about a food product’s contents.
IBM Artificial Intelligence Fundamentals certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Artificial Intelligence Fundamentals graded quizzes and final assessments, earn IBM Artificial Intelligence Fundamentals digital credential and badge.