AI ethics evidence index Search
Source-linked daily record
Evidence record 5018 · automatically gathered

Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthiness has become a central challenge. However, the core trustworthy AI objectives, such as fairness, robustness, privacy, and explainability, are hard to achieve simultaneously, especially while preserving utility. This position paper argues that causality is necessary to understand and balance trade-offs in performance an

Record details

Published: 4 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Privacy · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Privacy

FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings

arXiv · 5 May 2026

Shared topic: Bias & fairness · Shared topic: Privacy

TRUST: A Framework for Decentralized AI Service v.0.1

arXiv · 29 April 2026

Shared topic: Bias & fairness · Shared topic: Privacy

TIER: Trajectory-Invariant Explanation Regularization for Membership Privacy

arXiv · 3 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives

arXiv fairness query · 8 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

arXiv · 21 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification

arXiv cs.CR (AI security) · 23 July 2026

How to cite this record

ethics.ai (4 May 2026), “Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution,” evidence record 5018, https://ethics.ai/record/5018 (originally published by arXiv).

JSON

Use and limitations

This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.