NCA-GENM Question 204
Select 3You are developing a multimodal AI system that processes text, images, and sensor data to assist in emergency response scenarios. To ensure the system is ethical, energy-conscious, and reliable, what steps should you prioritize during the design and implementation phase?
- A
Incorporate explainability methods to allow users to understand how the system makes decisions.
- B
Optimize the model's architecture to minimize energy consumption during training and inference.
- C
Collect and use data without considering potential biases, as the model will generalize during training.
- D
Implement fairness audits to ensure the system performs equally well across diverse populations.
- E
Focus solely on achieving the highest accuracy, as ethical considerations can be addressed post-deployment.
Show answer and explanation
Correct answers: A, B, D
Explanation
To create ethical, energy-conscious, and reliable AI systems, it is essential to prioritize transparency, energy efficiency, and fairness during the design and implementation phase. Explainability, fairness audits, and energy optimization are critical steps to achieving these goals. Ignoring biases or delaying ethical considerations until post-deployment can result in harm or mistrust, undermining the purpose of responsible AI.
- A. Correct.
Incorporating explainability methods ensures transparency and allows users to trust the system by understanding its decision-making process. This is crucial for ethical AI systems.
- B. Correct.
Optimizing the model's architecture for energy efficiency directly addresses the need for energy-conscious AI systems, reducing their environmental impact.
- C. Incorrect.
Ignoring potential biases during data collection can lead to unfair and unreliable systems. Addressing biases is a critical step during the design phase.
- D. Correct.
Conducting fairness audits ensures that the system works equitably across different demographics, which is essential for ethical AI.
- E. Incorrect.
Focusing only on accuracy without considering ethical or fairness aspects can lead to biased or opaque systems, which contradicts the principles of ethical AI.