The emphasis on sustainable computing, bias prevention, and model explainability corresponds to which practice?

The emphasis on sustainable computing, bias prevention, and model explainability corresponds to which practice?

• A. Few-shot learning
• B. AI Governance
• C. Responsible AI practices
• D. ROI analysis in AI

Answer: (C)

Focusing on sustainable computing, bias prevention, and model explainability points to responsible AI practices. Sustainable computing targets energy efficiency and responsible resource use in training and inference. Bias prevention involves careful data handling, diverse representation, and ongoing fairness evaluation to avoid unequal outcomes. Model explainability ensures decisions are understandable, supporting accountability, debugging, and trust. Together, these elements build a practical, value-driven approach to AI that prioritizes safety, fairness, and transparency throughout the system’s life cycle. AI governance covers policies and oversight but is broader than this specific emphasis. The combination described aligns best with responsible AI practices, which translate ethical principles into concrete engineering and deployment actions. Few-shot learning and ROI analysis do not center these ethical and sustainability concerns.

Focusing on sustainable computing, bias prevention, and model explainability points to responsible AI practices. Sustainable computing targets energy efficiency and responsible resource use in training and inference. Bias prevention involves careful data handling, diverse representation, and ongoing fairness evaluation to avoid unequal outcomes. Model explainability ensures decisions are understandable, supporting accountability, debugging, and trust. Together, these elements build a practical, value-driven approach to AI that prioritizes safety, fairness, and transparency throughout the system’s life cycle.

AI governance covers policies and oversight but is broader than this specific emphasis. The combination described aligns best with responsible AI practices, which translate ethical principles into concrete engineering and deployment actions. Few-shot learning and ROI analysis do not center these ethical and sustainability concerns.