Which practice focuses on sustainable computing, bias prevention, and model explainability in AI?

• A. Responsible AI practices
• B. AI Governance
• C. Creative projects in Gen AI
• D. Change of thought prompting

Answer: (A)

Responsible AI practices focus on integrating sustainability, fairness, and transparency into the full lifecycle of AI systems. Sustainable computing means choosing approaches that minimize energy use and optimize resources during training and inference, such as efficient algorithms, model optimization, and hardware considerations. Bias prevention involves data governance, fairness metrics, and ongoing monitoring to avoid discriminatory outcomes. Model explainability is about making why a model makes a decision understandable, using interpretable models or explanations that stakeholders can trust.

Together, these elements define responsible AI practices, which is why this option best fits a question about sustainable computing, bias prevention, and explainability. The other choices cover broader governance, experimentation, or prompting techniques, which don’t specifically center these three focused concerns.