This can be done in the following ways:

1. Interpretable Models: Use AI models that are intrinsically interpretable, such as decision trees or linear models, to help stakeholders understand how decisions are produced using clear rules and features.
2. Feature Importance: Provide insights into the importance of features in AI models to emphasize which elements contribute the most significantly to predictions or choices, hence improving transparency.
3. Explainable AI Techniques: Use techniques such as LIME (Local Interpretable Model agnostic Explanations) or SHAP (SHapley Additive Explanations) to create explanations for specific predictions, hence increasing transparency while maintaining accuracy.
4. Documentation and reporting: Keep detailed records of AI system development, training data, and performance metrics to ensure transparency in implementation and consequences.
5. Ethical and Regulatory Compliance: Follow ethical norms and regulatory criteria for AI deployment, ensuring that AI system judgments are lawful and ethical, and can be justified to stakeholders.
6. Continuous Monitoring and Stakeholder Engagement: Implement tools for continuing monitoring and auditing of AI systems to detect biases, errors, or performance degradation, ensuring transparency and accuracy throughout time. Involve stakeholders in the creation and testing of AI systems, such as end users, domain experts, and impacted communities, to receive feedback and ensure that their requirements and expectations are met.