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Found 18 papers, 11 papers with code
Towards Non-Adversarial Algorithmic Recourse
We subsequently investigate how different components in the objective functions, e. g., the machine learning model or cost function used to measure distance, determine whether the outcome can be considered an adversarial example or not.
In-Context Unlearning: Language Models as Few Shot Unlearners
In this work, we propose a new class of unlearning methods for LLMs we call ''In-Context Unlearning'', providing inputs in context and without having to update model parameters.
Gaussian Membership Inference Privacy
In particular, we derive a parametric family of $f$-MIP guarantees that we refer to as $\mu$-Gaussian Membership Inference Privacy ($\mu$-GMIP) by theoretically analyzing likelihood ratio-based membership inference attacks on stochastic gradient descent (SGD).
On the Privacy Risks of Algorithmic Recourse
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals.
Decomposing Counterfactual Explanations for Consequential Decision Making
In this work, we develop \texttt{DEAR} (DisEntangling Algorithmic Recourse), a novel and practical recourse framework that bridges the gap between the IMF and the strong causal assumptions.
I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data
In this work, we show that the decision not to share data can be considered as information in itself that should be protected to respect users' privacy.
Language Models are Realistic Tabular Data Generators
Tabular data is among the oldest and most ubiquitous forms of data.
On the Trade-Off between Actionable Explanations and the Right to be Forgotten
Thus, our work raises fundamental questions about the compatibility of "the right to an actionable explanation" in the context of the "right to be forgotten", while also providing constructive insights on the determining factors of recourse robustness.
OpenXAI: Towards a Transparent Evaluation of Model Explanations
OpenXAI comprises of the following key components: (i) a flexible synthetic data generator and a collection of diverse real-world datasets, pre-trained models, and state-of-the-art feature attribution methods, and (ii) open-source implementations of eleven quantitative metrics for evaluating faithfulness, stability (robustness), and fairness of explanation methods, in turn providing comparisons of several explanation methods across a wide variety of metrics, models, and datasets.
Rethinking Stability for Attribution-based Explanations
As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are stable, e. g., robust to infinitesimal perturbations to an input.
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
To this end, we propose a novel objective function which simultaneously minimizes the gap between the achieved (resulting) and desired recourse invalidation rates, minimizes recourse costs, and also ensures that the resulting recourse achieves a positive model prediction.
Deep Neural Networks and Tabular Data: A Survey
Moreover, we discuss deep learning approaches for generating tabular data, and we also provide an overview over strategies for explaining deep models on tabular data.
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods.
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice.
Gaussian Experts Selection using Graphical Models
Imposing the \emph{conditional independence assumption} (CI) between the experts renders the aggregation of different expert predictions time efficient at the cost of poor uncertainty quantification.
On Counterfactual Explanations under Predictive Multiplicity
In this work, we derive a general upper bound for the costs of counterfactual explanations under predictive multiplicity.
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Feature selection can be a crucial factor in obtaining robust and accurate predictions.
Learning Model-Agnostic Counterfactual Explanations for Tabular Data
On one hand, we suggest to complement the catalogue of counterfactual quality measures [1] using a criterion to quantify the degree of difficulty for a certain counterfactual suggestion.
