Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
Fuente:
arXiv
Saved in:
| Main Authors: | Binkyte, Ruta, Sheth, Ivaxi, Jin, Zhijing, Havaei, Mohammad, Schölkopf, Bernhard, Fritz, Mario |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
by: Binkyte, Ruta, et al.
Published: (2026)
by: Binkyte, Ruta, et al.
Published: (2026)
Safety Must Precede the Deployment of Open-Ended AI
by: Sheth, Ivaxi, et al.
Published: (2025)
by: Sheth, Ivaxi, et al.
Published: (2025)
LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
by: Afonja, Tejumade, et al.
Published: (2024)
by: Afonja, Tejumade, et al.
Published: (2024)
IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
by: Sheth, Ivaxi, et al.
Published: (2026)
by: Sheth, Ivaxi, et al.
Published: (2026)
Causality can systematically address the monsters under the bench(marks)
by: Leeb, Felix, et al.
Published: (2025)
by: Leeb, Felix, et al.
Published: (2025)
Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews
by: Vasu, Sai Suresh Macharla, et al.
Published: (2025)
by: Vasu, Sai Suresh Macharla, et al.
Published: (2025)
On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis
by: Binkyte, Ruta, et al.
Published: (2022)
by: Binkyte, Ruta, et al.
Published: (2022)
Interactional Fairness in LLM Multi-Agent Systems: An Evaluation Framework
by: Binkyte, Ruta
Published: (2025)
by: Binkyte, Ruta
Published: (2025)
ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocols
by: Sheth, Arnav, et al.
Published: (2025)
by: Sheth, Arnav, et al.
Published: (2025)
Causal Responsibility Attribution for Human-AI Collaboration
by: Qi, Yahang, et al.
Published: (2024)
by: Qi, Yahang, et al.
Published: (2024)
Improving Large Language Model Safety with Contrastive Representation Learning
by: Simko, Samuel, et al.
Published: (2025)
by: Simko, Samuel, et al.
Published: (2025)
BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables
by: Binkyte, Ruta, et al.
Published: (2023)
by: Binkyte, Ruta, et al.
Published: (2023)
Stargazer: A Scalable Model-Fitting Benchmark Environment for AI Agents under Astrophysical Constraints
by: Liu, Xinge, et al.
Published: (2026)
by: Liu, Xinge, et al.
Published: (2026)
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)
by: Rajendran, Goutham, et al.
Published: (2024)
Causal Discovery Under Local Privacy
by: Binkytė, Rūta, et al.
Published: (2023)
by: Binkytė, Rūta, et al.
Published: (2023)
CausalCite: A Causal Formulation of Paper Citations
by: Kumar, Ishan, et al.
Published: (2023)
by: Kumar, Ishan, et al.
Published: (2023)
Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables
by: Sheth, Ivaxi, et al.
Published: (2024)
by: Sheth, Ivaxi, et al.
Published: (2024)
Causality for Natural Language Processing
by: Jin, Zhijing
Published: (2025)
by: Jin, Zhijing
Published: (2025)
CLadder: Assessing Causal Reasoning in Language Models
by: Jin, Zhijing, et al.
Published: (2023)
by: Jin, Zhijing, et al.
Published: (2023)
Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries
by: Ceraolo, Roberto, et al.
Published: (2024)
by: Ceraolo, Roberto, et al.
Published: (2024)
Inspectable AI for Science: A Research Object Approach to Generative AI Governance
by: Binkyte, Ruta, et al.
Published: (2026)
by: Binkyte, Ruta, et al.
Published: (2026)
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
by: Farnadi, Golnoosh, et al.
Published: (2024)
by: Farnadi, Golnoosh, et al.
Published: (2024)
Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs
by: Labroo, Arya, et al.
Published: (2026)
by: Labroo, Arya, et al.
Published: (2026)
Deep Backtracking Counterfactuals for Causally Compliant Explanations
by: Kladny, Klaus-Rudolf, et al.
Published: (2023)
by: Kladny, Klaus-Rudolf, et al.
Published: (2023)
Can Large Language Models Infer Causation from Correlation?
by: Jin, Zhijing, et al.
Published: (2023)
by: Jin, Zhijing, et al.
Published: (2023)
Analyzing the Role of Semantic Representations in the Era of Large Language Models
by: Jin, Zhijing, et al.
Published: (2024)
by: Jin, Zhijing, et al.
Published: (2024)
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
by: Reizinger, Patrik, et al.
Published: (2024)
by: Reizinger, Patrik, et al.
Published: (2024)
Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
by: Kekić, Armin, et al.
Published: (2025)
by: Kekić, Armin, et al.
Published: (2025)
Causal vs. Anticausal merging of predictors
by: Mejia, Sergio Hernan Garrido, et al.
Published: (2025)
by: Mejia, Sergio Hernan Garrido, et al.
Published: (2025)
Out-of-Variable Generalization for Discriminative Models
by: Guo, Siyuan, et al.
Published: (2023)
by: Guo, Siyuan, et al.
Published: (2023)
Computational Arbitrage in AI Model Markets
by: Olmedo, Ricardo, et al.
Published: (2026)
by: Olmedo, Ricardo, et al.
Published: (2026)
A Probabilistic Model Behind Self-Supervised Learning
by: Bizeul, Alice, et al.
Published: (2024)
by: Bizeul, Alice, et al.
Published: (2024)
Causal Component Analysis
by: Wendong, Liang, et al.
Published: (2023)
by: Wendong, Liang, et al.
Published: (2023)
Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering
by: Kladny, Klaus-Rudolf, et al.
Published: (2024)
by: Kladny, Klaus-Rudolf, et al.
Published: (2024)
The Essential Role of Causality in Foundation World Models for Embodied AI
by: Gupta, Tarun, et al.
Published: (2024)
by: Gupta, Tarun, et al.
Published: (2024)
Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems
by: Khandelwal, Vedant, et al.
Published: (2024)
by: Khandelwal, Vedant, et al.
Published: (2024)
CausalGraph2LLM: Evaluating LLMs for Causal Queries
by: Sheth, Ivaxi, et al.
Published: (2024)
by: Sheth, Ivaxi, et al.
Published: (2024)
Implicit Personalization in Language Models: A Systematic Study
by: Jin, Zhijing, et al.
Published: (2024)
by: Jin, Zhijing, et al.
Published: (2024)
SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models
by: Imtiaz, Syed Usama, et al.
Published: (2026)
by: Imtiaz, Syed Usama, et al.
Published: (2026)
Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games
by: Piedrahita, David Guzman, et al.
Published: (2025)
by: Piedrahita, David Guzman, et al.
Published: (2025)
Similar Items
-
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
by: Binkyte, Ruta, et al.
Published: (2026) -
Safety Must Precede the Deployment of Open-Ended AI
by: Sheth, Ivaxi, et al.
Published: (2025) -
LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
by: Afonja, Tejumade, et al.
Published: (2024) -
IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
by: Sheth, Ivaxi, et al.
Published: (2026) -
Causality can systematically address the monsters under the bench(marks)
by: Leeb, Felix, et al.
Published: (2025)