What is a key challenge that affects synthetic data generation techniques?
Answer(s): C
A key challenge of synthetic data is fidelity to the "long tail" of data. Because generators prioritize the most frequent statistical patterns, they often fail to replicate the rare edge cases and complex nuances found in real- world data, which can result in AI models that perform poorly when they encounter unusual but critical real- world situations.
What is the primary purpose of quality filtering in training data preparation?
Answer(s): B
The primary goal of quality filtering is to ensure that the AI model learns from accurate, coherent, andrepresentative data. By removing spam, gibberish, and low-value content, developers improve the model's reasoning capabilities and reduce the likelihood of the AI generating nonsensical or harmful outputs.
Sampling bias in AI occurs when:
Sampling bias occurs when the training dataset is a flawed "subset" of reality. If the data used to teach the AI lacks the diversity or range of the actual population it will encounter, the system will produce skewed or inaccurate results for the underrepresented groups.
A credit risk model was trained using financial data from five years ago. What data quality problem might cause it to make poor predictions about customers today?
The primary problem is a lack of timeliness. Because economic conditions and consumer behaviors change over time, a model trained on five-year-old data suffers from data drift. To remain accurate, credit models require fresh data that reflects the current financial landscape.
What is the relationship between model explainability and AI trustworthiness?
Answer(s): D
Explainability is a prerequisite for trustworthiness. For a system to be considered "trustworthy," it must be transparent enough for experts to audit its logic, for users to understand its results, and for regulators to ensure it complies with safety and fairness standards. Without explainability, "trust" is just blind faith, which is unacceptable in high-stakes AI applications.
When would you choose LIME over SHAP for model explanation?
Answer(s): A
The primary reason to choose LIME over SHAP is computational efficiency. While SHAP is more mathematically consistent and "fair," it is often too slow for large datasets or real-time applications. LIME offers a much faster, "local" approximation that is perfect when you need quick insights without the heavy processing cost.
The 2021 Anthony Bourdain documentary deepfake controversy highlighted:
The Bourdain controversy is a landmark case in AI Ethics, illustrating the risks of using synthetic media without informed consent and the lack of transparency regarding the use of "deepfake" audio in creative works.
What does automated bias detection provide?
Automated bias detection provides continuous monitoring. Unlike manual or one-time checks, automated tools scan model performance in real-time to ensure the system remains ethically compliant and fair as real-world data evolves.
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