AWS Certified AI Practitioner AIF-C01 Practice Question
A data science team needs to share a text-classification model's reasoning with non-technical marketing analysts. According to human-centered design principles for explainable AI, which approach best contextualizes the explanation for these users?
Describe how the model's output was influenced by customer-facing phrases and campaign language familiar to the marketing team.
Provide the full set of gradient and attention weight values generated during inference.
Walk the analysts through the mathematical derivation of the model's loss function and optimization steps.
Release the entire training dataset so analysts can examine every labeled example.
Human-centered design stresses that explanations should be meaningful to the intended audience. Translating the model's reasoning into marketing terms-such as highlighting the specific keywords or phrases that drove a positive or negative sentiment label-anchors the explanation in concepts the analysts already use. Exposing raw attention weights, lengthy mathematical derivations, or the complete training dataset overwhelms or confuses non-technical users and does not improve their understanding or trust.
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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