Post Your Answer

Developing robust and transparent explainable AI models that align with ethical principles to address the black-box problem, promote accountability, and build trust between AI systems and their users requires integrating interpretable AI techniques, diverse and representative datasets to mitigate bias, ethical frameworks like FATE, and human-in-the-loop approaches for critical applications. Industry-wide standards for explainability must be established, and continuous monitoring and auditing of AI systems' performance should be conducted to ensure transparency and accountability.