Implementing transparency and explainability
Transparency and explainability are cardinal characteristics of any trustworthy AI system. Indeed, explanations of how AI models arrive at their decisions in building content would provide insight for the users into the reasoning that led to such output, thereby fostering trust and confidence in the reliability of the system.
Consider the travel agent scenario, where a generative AI system recommends personalized travel itineraries based on user preferences and historical data. Transparency and explainability are crucial for building trust in such a system. Users may want to understand why certain destinations or activities were recommended over others, and how the AI factored in their preferences, budget constraints, and travel histories.
As we saw earlier, techniques such as saliency maps, feature importance, and natural language explanations are some of the XAI techniques that could be used to facilitate transparency and interpretability...
