Cover Image

Explainable AI Isn't Enough! Rethinking Algorithmic Contestability

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Freiesleben, Timo, Meding, Kristof, König, Gunnar
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

It's complicated. The relationship of algorithmic fairness and non-discrimination provisions for high-risk systems in the EU AI Act
by: Meding, Kristof
Published: (2025)

Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms
by: Meding, Kristof, et al.
Published: (2023)

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

Beware! The AI Act Can Also Apply to Your AI Research Practices
by: Wernick, Alina, et al.
Published: (2025)

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

What constitutes a Deep Fake? The blurry line between legitimate processing and manipulation under the EU AI Act
by: Meding, Kristof, et al.
Published: (2024)

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

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

ML Interpretability: Simple Isn't Easy
by: Räz, Tim
Published: (2022)

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

When Privacy Isn't Synthetic: Hidden Data Leakage in Generative AI Models
by: Mustaqim, S. M., et al.
Published: (2025)

AI Isn't Creating Anything
by: Lee Skallerup Bessette
Published: (2025)

When Trust Isn't Enough.
by: Behrman, Sara
Published: (1998)

Why Isn't Relational Learning Taking Over the World?
by: Poole, David
Published: (2025)

Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data
by: Nolte, Henrik, et al.
Published: (2025)

Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models
by: Favero, Alessandro, et al.
Published: (2025)

Performative Validity of Recourse Explanations
by: König, Gunnar, et al.
Published: (2025)

Causal Inference Isn't Special: Why It's Just Another Prediction Problem
by: Fernández-Loría, Carlos
Published: (2025)

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs
by: Chen, Yiwei, et al.
Published: (2025)

CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
by: Dandl, Susanne, et al.
Published: (2024)

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

When Fairness Isn't Statistical: The Limits of Machine Learning in Evaluating Legal Reasoning
by: Barale, Claire, et al.
Published: (2025)

Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
by: Xu, Xiaoyu, et al.
Published: (2025)

When Correct Isn't Usable: Improving Structured Output Reliability in Small Language Models
by: Galeone, Cosimo, et al.
Published: (2026)

Recall Isn't Enough: Bounding Commitments in Personalized Language Systems
by: Tang, Rui, et al.
Published: (2026)

Reasoning Isn't Enough: Examining Truth-Bias and Sycophancy in LLMs
by: Barkett, Emilio, et al.
Published: (2025)

What You Read Isn't What You Hear: Linguistic Sensitivity in Deepfake Speech Detection
by: Nguyen, Binh, et al.
Published: (2025)

AI Isn't Creating Anything
by: Lee Skallerup Bessette
Published: (2025)

Relevance Isn't All You Need: Scaling RAG Systems With Inference-Time Compute Via Multi-Criteria Reranking
by: LeVine, Will, et al.
Published: (2025)

Rethinking Explainability in the Era of Multimodal AI
by: Agarwal, Chirag
Published: (2025)

Explainable AI Systems Must Be Contestable: Here's How to Make It Happen
by: Moreira, Catarina, et al.
Published: (2025)

Attention Isn't All You Need for Emotion Recognition:Domain Features Outperform Transformers on the EAV Dataset
by: Guragain, Anmol
Published: (2026)

Strong Reasoning Isn't Enough: Evaluating Evidence Elicitation in Interactive Diagnosis
by: Long, Zhuohan, et al.
Published: (2026)

When Alignment Isn't Enough: Response-Path Attacks on LLM Agents
by: Luo, Mingyu, et al.
Published: (2026)

Knowing the Answer Isn't Enough: Fixing Reasoning Path Failures in LVLMs
by: Wang, Chaoyang, et al.
Published: (2025)

From Metacognition to Computable Wisdom: Why "Thinking About Thinking" Isn't Enough for Agentic AI
by: Figurelli, Rogério
Published: (2026)

Knowing Isn't Understanding: Re-grounding Generative Proactivity with Epistemic and Behavioral Insight
by: Kaur, Kirandeep, et al.
Published: (2026)

When Standard Newborn Screening Isn't Enough: Diagnostic Challenges in the Age of Globalization
by: Marina Ortúzar Menéndez, et al.
Published: (2026)

Awake ECMO for Mid‐Tracheal Obstruction: When a Tracheostomy Isn't Enough
by: Jacob Beiriger, et al.
Published: (2026)

Rethinking Algorithmic Fairness for Human-AI Collaboration
by: Ge, Haosen, et al.
Published: (2023)