Explainability

Poster presentation highlighting our project on scaling up causal recourse by designing a novel amortized framework based on causal generative models and neural network based causal intervention predictors.

We explore improving the practicality of providing causal recourse explanations through a novel neural network model-based automation framework.

Algorithms are increasingly used to automate large-scale decision-making processes, e.g., online platforms that make instant decisions …

An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural …