Another technique is post hoc explainability, where AI systems generate explanations for their decisions after the fact. This approach aims to provide a human-readable explanation of the decision-making process, enabling users to assess the validity and fairness of the AI system's outputs.
Additionally, there are efforts to integrate india database transparency into the design and development of AI systems from the beginning. This includes adopting ethical guidelines, incorporating user feedback, and ensuring that AI systems are accountable and fair.
Explainable AI offers several benefits beyond trust and transparency. It enables users to identify biases and discriminatory patterns in AI systems, leading to fairer outcomes. Explainability also helps in debugging and improving the performance of AI algorithms by providing insights into their strengths and weaknesses.