AWS Certified AI Practitioner · Free Practice Question Medium
Question 37
In the context of AI governance, which two practices are essential for ensuring that AI systems are developed and deployed responsibly within an organization? (Select Two)
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A
Prioritizing the speed of AI deployment over thorough testing and ethical considerations
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B
Implementing transparency mechanisms to ensure that AI decision-making processes are understandable and explainable
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C
Limiting AI model training to publicly available datasets to avoid data privacy concerns
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D
Relying on technical performance metrics, such as accuracy, to evaluate the effectiveness of AI systems
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E
Establishing clear accountability frameworks that define who is responsible for AI outcomes
Reveal correct answers
Correct answers: B, E
Explanation
1. Implementing Transparency Mechanisms: Transparency is crucial in AI governance because it makes the decision-making processes of AI systems understandable and explainable to stakeholders. When AI models operate transparently, it becomes easier to trace how decisions are made, which is vital for building trust among users and stakeholders. Transparent AI systems allow organizations to:
Understand Decision Paths: By knowing how an AI system arrives at a particular decision, organizations can identify and rectify potential biases or errors in the process.
Facilitate Accountability: Transparency ensures that there is a clear understanding of how outcomes are produced, which is essential for accountability.
Enhance Compliance: Many industries are subject to regulations that require transparency in automated decision-making. Implementing transparency mechanisms helps organizations comply with these legal requirements.
2. Establishing Clear Accountability Frameworks: Accountability frameworks define who is responsible for various aspects of AI system development, deployment, and outcomes. Clear accountability is essential to ensure that:
Responsibilities are Defined: Assigning specific roles and responsibilities prevents gaps in oversight and ensures that all aspects of AI governance are covered.
Ethical Standards are Maintained: With defined accountability, organizations can enforce ethical guidelines and standards consistently across all AI projects.
Issues are Addressed Promptly: When accountability is clear, any issues or unintended consequences arising from AI systems can be addressed swiftly and effectively.
References:
A.
Speed should not be prioritized over thorough testing and ethical considerations, as this can lead to unintended consequences.
B.
Implementing transparency mechanisms is crucial in AI governance to ensure that decisions made by AI systems can be understood and explained, which builds trust and accountability.
C.
While data privacy is important, limiting training to publicly available datasets may not always be sufficient to address privacy concerns or produce high-quality AI models.
D.
Focusing solely technical performance metrics ignores other crucial aspects like ethical considerations and social impact.
E.
Clear accountability frameworks are essential to define responsibilities and ensure that any negative outcomes from AI systems are properly addressed and managed.
Discussion
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