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Found 8 papers, 2 papers with code
Feature Attributions and Counterfactual Explanations Can Be Manipulated
As machine learning models are increasingly used in critical decision-making settings (e. g., healthcare, finance), there has been a growing emphasis on developing methods to explain model predictions.
Counterfactual Explanations Can Be Manipulated
In this work, we introduce the first framework that describes the vulnerabilities of counterfactual explanations and shows how they can be manipulated.
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
In this work, we analyze various sources of gender biases in online hiring platforms, including the job context and inherent biases of employers and establish how these factors interact with ranking algorithms to affect hiring decisions.
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
In this paper, we address the aforementioned challenges by developing a novel Bayesian framework for generating local explanations along with their associated uncertainty.
From Predictions to Decisions: Using Lookahead Regularization
Machine learning is a powerful tool for predicting human-related outcomes, from credit scores to heart attack risks.
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous.
Learning Key-Value Store Design
The critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as fundamentally different data structures to be seen as views of the very same overall design space, and 2) allow seeing new data structure designs with performance properties that are not feasible by existing designs.
Learning Representations by Humans, for Humans
When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy.
