Cover Image

I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Leemann, Tobias, Pawelczyk, Martin, Eberle, Christian Thomas, Kasneci, Gjergji
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Artificial Intelligence Computers and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Non-Adversarial Algorithmic Recourse
by: Leemann, Tobias, et al.
Published: (2024)

Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers
by: Leemann, Tobias, et al.
Published: (2024)

Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers
by: Kasneci, Gjergji, et al.
Published: (2024)

Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
by: Kirchhof, Michael, et al.
Published: (2025)

TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data
by: Berti, Leonardo, et al.
Published: (2025)

Where Paths Split: Localized, Calibrated Control of Moral Reasoning in Large Language Models
by: Yuan, Chenchen, et al.
Published: (2026)

Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs
by: Yang, Shuo, et al.
Published: (2025)

Emergent Abilities in Large Language Models: A Survey
by: Berti, Leonardo, et al.
Published: (2025)

Machine Unlearning Fails to Remove Data Poisoning Attacks
by: Pawelczyk, Martin, et al.
Published: (2024)

Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
by: Zhang, Zheyu, et al.
Published: (2026)

CURE: Controlled Unlearning for Robust Embeddings -- Mitigating Conceptual Shortcuts in Pre-Trained Language Models
by: Kocak, Aysenur, et al.
Published: (2025)

Say My Name: a Model's Bias Discovery Framework
by: Ciranni, Massimiliano, et al.
Published: (2024)

EvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Models
by: Dhaini, Mahdi, et al.
Published: (2025)

Assessing the Real-World Utility of Explainable AI for Arousal Diagnostics: An Application-Grounded User Study
by: Kraft, Stefan, et al.
Published: (2025)

Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
by: Dhaini, Mahdi, et al.
Published: (2025)

Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
by: Rong, Yao, et al.
Published: (2022)

Train Once, Answer All: Many Pretraining Experiments for the Cost of One
by: Bordt, Sebastian, et al.
Published: (2025)

The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI
by: Schirmer, Miriam, et al.
Published: (2024)

Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education
by: Bewersdorff, Arne, et al.
Published: (2024)

Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule
by: Zhao, Xuan, et al.
Published: (2024)

In-Context Unlearning: Language Models as Few Shot Unlearners
by: Pawelczyk, Martin, et al.
Published: (2023)

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

Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers
by: Abramov, Roman, et al.
Published: (2025)

Does the Model Say What the Data Says? A Simple Heuristic for Model Data Alignment
by: Salgado, Henry, et al.
Published: (2025)

Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models
by: Pawelczyk, Martin, et al.
Published: (2024)

The LLM Has Left The Chat: Evidence of Bail Preferences in Large Language Models
by: Ensign, Danielle, et al.
Published: (2025)

Investigating Thematic Patterns and User Preferences in LLM Interactions using BERTopic
by: Bhandarkar, Abhay, et al.
Published: (2025)

Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks
by: Kasneci, Enkelejda, et al.
Published: (2026)

Learning the Value Systems of Societies from Preferences
by: Holgado-Sánchez, Andrés, et al.
Published: (2025)

Enhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning
by: Kuo, Ming, et al.
Published: (2024)

Authority Inversion in LLM-Mediated Ubiquitous Systems: When Models Trust Users Over Sensors
by: Zhang, Long, et al.
Published: (2026)

What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds
by: Bothmann, Ludwig, et al.
Published: (2022)

Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?
by: Zakazov, Ivan, et al.
Published: (2024)

Fairness in Ranking: Robustness through Randomization without the Protected Attribute
by: Kliachkin, Andrii, et al.
Published: (2024)

Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI
by: Eberle, Oliver, et al.
Published: (2023)

Multimodal Assessment of Classroom Discourse Quality: A Text-Centered Attention-Based Multi-Task Learning Approach
by: Hou, Ruikun, et al.
Published: (2025)

NudgeRank: Digital Algorithmic Nudging for Personalized Health
by: Chiam, Jodi, et al.
Published: (2024)

Influence of Recommender Systems on Users: A Dynamical Systems Analysis
by: Lankireddy, Prabhat, et al.
Published: (2024)

Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring
by: Ovi, Md Sultanul Islam, et al.
Published: (2025)

Training Foundation Models as Data Compression: On Information, Model Weights and Copyright Law
by: Franceschelli, Giorgio, et al.
Published: (2024)