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Found 6 papers, 0 papers with code
Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation
We are able to enforce conditional independence of the diffusion autoencoder latent representation with respect to any protected attribute under the equalized odds constraint and show that this approach enables causal image generation with controllable latent spaces.
Combining Counterfactuals With Shapley Values To Explain Image Models
With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task.
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces
Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases.
Predicting Airbnb Rental Prices Using Multiple Feature Modalities
Figuring out the price of a listed Airbnb rental is an important and difficult task for both the host and the customer.
Pitfalls of Explainable ML: An Industry Perspective
The goal of explainable ML is to intuitively explain the predictions of a ML system, while adhering to the needs to various stakeholders.
Accurate and Intuitive Contextual Explanations using Linear Model Trees
We use a Generative Adversarial Network for synthetic data generation and train a piecewise linear model in the form of Linear Model Trees to be used as the surrogate model. In addition to individual feature attributions, we also provide an accompanying context to our explanations by leveraging the structure and property of our surrogate model.
