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Many AI initiatives falter due to a lack of trust. Subject matter experts (SMEs) often second-guess model predictions, especially when models are perceived as "black boxes" that omit explanations for their outputs. When predictions are questioned but the rationale is unclear, this skepticism hampers user adoption, diminishes confidence, and ultimately leads to project failure. The core issue is trust, which relies heavily on explainability and transparency—hallmarks of responsible AI.
The Pillars of Responsible AI
Responsible AI encompasses multiple interconnected components that collectively ensure AI systems are trustworthy, ethical, and aligned with business and regulatory expectations. Key elements include: