680 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in counterfactual
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.
Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".
We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims.
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
We then provide a mechanism to generate the smallest set of changes that will improve an individual's outcome.
Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on.