AWS Certified AI Practitioner AIF-C01 Practice Question
A data science team wants to publish full details about a language model's architecture, training data, and hyper-parameters to increase stakeholder trust. When considering the tradeoff between transparency and safety, what new risk could this disclosure introduce?
Human oversight requirements would be completely eliminated for future deployments.
All existing biases in the model's outputs would be permanently removed.
The model could become more vulnerable to adversarial attacks that trigger harmful behavior.
The model's carbon footprint would automatically increase during future training runs.
Making all design and data details public improves transparency, but it also gives malicious actors the information they need to craft adversarial prompts or data that deliberately cause harmful or misleading outputs. This heightened vulnerability to adversarial attacks is a recognized safety concern. Greater transparency does not, by itself, lower carbon emissions, remove bias, or eliminate the need for human oversight, so those options do not represent the primary new risk created by disclosure.
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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