RAISE: A Unified Framework for Responsible AI Scoring and Evaluation

No single "best" AI model exists across responsibility dimensions: The study found that different models excelled in different areas—the MLP was most sustainable and robust, the Transformer was most explainable and fair, and the Tabular ResNet offered balance—demonstrating that organizations cannot optimize for accuracy alone when deploying AI in high-stakes domains like finance and healthcare.

Environmental cost is a hidden responsibility trade-off: The Transformer model achieved superior explainability and fairness performance but came with "very high environmental cost," revealing that responsible AI evaluation must account for sustainability alongside traditional fairness and accuracy metrics.

Responsibility requires multi-dimensional evaluation: RAISE's framework quantifies AI systems across four key dimensions (explainability, fairness, robustness, and sustainability) rather than relying on predictive accuracy alone, suggesting that organizations should adopt similarly comprehensive evaluation approaches before deploying AI in high-stakes decisions.

Model selection involves necessary trade-offs: The research demonstrates that improving one responsibility dimension may compromise another, meaning organizations must consciously decide which dimensions matter most for their specific use case rather than assuming all responsible AI goals can be simultaneously maximized.

Structured evaluation frameworks are now available: The authors made their RAISE implementation publicly available, providing organizations with a practical tool to systematically evaluate and compare AI models across multiple responsibility criteria before deployment.