Krishna discusses the importance of understanding model predictions, particularly in fraud detection scenarios. By probing models with counterfactual inputs, insights can be gleaned about how different features affect outcomes. He also touches on the application of game theory, like Shapley value, to systematically attribute the influence of various inputs on the model's decisions. The conversation pivots towards the challenge of applying these concepts to large language models.
