Imagen de Portada

Performative Validity of Recourse Explanations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	König, Gunnar, Fokkema, Hidde, Freiesleben, Timo, Mendler-Dünner, Celestine, von Luxburg, Ulrike
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Computers and Society
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

The Risks of Recourse in Binary Classification
por: Fokkema, Hidde, et al.
Publicado: (2023)

Performative Prediction: Past and Future
por: Hardt, Moritz, et al.
Publicado: (2023)

Improvement-Focused Causal Recourse (ICR)
por: König, Gunnar, et al.
Publicado: (2022)

Look-Ahead Reasoning on Learning Platforms
por: Zhu, Haiqing, et al.
Publicado: (2025)

Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists
por: Baumann, Joachim, et al.
Publicado: (2024)

Evaluating language models as risk scores
por: Cruz, André F., et al.
Publicado: (2024)

An engine not a camera: Measuring performative power of online search
por: Mendler-Dünner, Celestine, et al.
Publicado: (2024)

Algorithmic Collective Action in Machine Learning
por: Hardt, Moritz, et al.
Publicado: (2023)

Using predictive multiplicity to measure individual performance within the AI Act
por: Frohnapfel, Karolin, et al.
Publicado: (2026)

Auditing Local Explanations is Hard
por: Bhattacharjee, Robi, et al.
Publicado: (2024)

Disentangling Interactions and Dependencies in Feature Attribution
por: König, Gunnar, et al.
Publicado: (2024)

Establishing Construct Validity in LLM Capability Benchmarks Requires Nomological Networks
por: Freiesleben, Timo
Publicado: (2026)

KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education
por: Kim, Woojin, et al.
Publicado: (2026)

The Manifold Hypothesis for Gradient-Based Explanations
por: Bordt, Sebastian, et al.
Publicado: (2022)

Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
por: Freiesleben, Timo, et al.
Publicado: (2026)

Adjusting Pretrained Backbones for Performativity
por: Demirel, Berker, et al.
Publicado: (2024)

Time Can Invalidate Algorithmic Recourse
por: De Toni, Giovanni, et al.
Publicado: (2024)

The Benchmarking Epistemology: Construct Validity for Evaluating Machine Learning Models
por: Freiesleben, Timo, et al.
Publicado: (2025)

Prediction without Preclusion: Recourse Verification with Reachable Sets
por: Kothari, Avni, et al.
Publicado: (2023)

Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity
por: Bell, Andrew, et al.
Publicado: (2024)

Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden
por: Barrainkua, Ainhize, et al.
Publicado: (2025)

Decline Now: A Combinatorial Model for Algorithmic Collective Action
por: Sigg, Dorothee, et al.
Publicado: (2024)

Artificial Neural Nets and the Representation of Human Concepts
por: Freiesleben, Timo
Publicado: (2023)

A New Paradigm for Counterfactual Reasoning in Fairness and Recourse
por: Bynum, Lucius E. J., et al.
Publicado: (2024)

Learning Recourse Costs from Pairwise Feature Comparisons
por: Rawal, Kaivalya, et al.
Publicado: (2024)

Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
por: Freiesleben, Timo, et al.
Publicado: (2022)

Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions
por: Fokkema, Hidde, et al.
Publicado: (2025)

Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration
por: Esfahani, Seyedehdelaram, et al.
Publicado: (2024)

Questioning the Survey Responses of Large Language Models
por: Dominguez-Olmedo, Ricardo, et al.
Publicado: (2023)

Stochastic wage suppression on gig platforms and how to organize against it
por: Stoica, Ana-Andreea, et al.
Publicado: (2026)

Informative Post-Hoc Explanations Only Exist for Simple Functions
por: Günther, Eric, et al.
Publicado: (2025)

How Much Can We Forget about Data Contamination?
por: Bordt, Sebastian, et al.
Publicado: (2024)

Rethinking Explainable Machine Learning as Applied Statistics
por: Bordt, Sebastian, et al.
Publicado: (2024)

Selective Explanations
por: Paes, Lucas Monteiro, et al.
Publicado: (2024)

ReasonX: Declarative Reasoning on Explanations
por: State, Laura, et al.
Publicado: (2026)

How to safely discard features based on aggregate SHAP values
por: Bhattacharjee, Robi, et al.
Publicado: (2025)

Understanding Disparities in Post Hoc Machine Learning Explanation
por: Mhasawade, Vishwali, et al.
Publicado: (2024)

EXAGREE: Mitigating Explanation Disagreement with Stakeholder-Aligned Models
por: Li, Sichao, et al.
Publicado: (2024)

The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models
por: Anderson, Joshua Wolff, et al.
Publicado: (2025)

DetoxLLM: A Framework for Detoxification with Explanations
por: Khondaker, Md Tawkat Islam, et al.
Publicado: (2024)