IEEE Journal on Emerging and Selected Topics in Circuits and Systems / 2024

An Overview of Trustworthy AI: Advances in IP Protection, Privacy-Preserving Federated Learning, Security Verification, and GAI Safety Alignment

Yue Zheng, Chip-Hong Chang, Shih-Hsu Huang, Pin-Yu Chen, S. Picek

AI SafetyFoundation ModelsLarge Language ModelsPopular and Landmark Papers

AI has undergone a remarkable evolution journey marked by groundbreaking milestones. Like any powerful tool, it can be turned into a weapon for devastation in the wrong hands. Understanding that no model is perfect, trustworthy AI is initiated with an intuitive aim to mitigate the harm it can inflict on people and society by prioritizing socially responsible AI ideation, design, development, and deployment towards effecting positive changes. The scope of trustworthy AI is encompassing, covering qualities such as safety, security, privacy, transparency, explainability, fairness, impartiality, robustness, reliability, and accountability. This overview paper anchors on recent advances in four research hotspots of trustworthy AI with compelling and challenging security, privacy, and safety issues. The topics discussed include the intellectual property protection of deep learning and generative models, the trustworthiness of federated learning, verification and testing tools of AI systems, and the safety alignment of generative AI systems. Through this comprehensive review, we aim to provide readers with an overview of the most up-to-date research problems and solutions. By presenting the rapidly evolving factors and constraints that motivate the emerging attack and defense strategies throughout the AI life-cycle, we hope to inspire more research effort into guiding AI technologies towards beneficial purposes with greater robustness against malicious use intent.

24 citations1 influential

Full paper

Read the original paper

Source page

A direct open-access PDF is not available in the database yet. Use the source page or learning resources below to open the complete paper from the publisher or index.

Learning resources

Google Scholar referencesGoogle Scholar Papers with Code searchPapers with Code Semantic Scholar paper pageSemantic Scholar YouTube explanationsYouTube

Reading state

Discuss in ChatGPT

Uses your own ChatGPT account. The paper context is copied into a tutor prompt before ChatGPT opens.

Preview prompt

You are my AI/ML research paper instructor. I want to deeply understand the paper below.

First, teach it in layers:
1. One-paragraph intuition.
2. Problem statement and why it mattered.
3. Key method, architecture, or algorithm.
4. Important equations or mechanisms, explained intuitively.
5. Experiments and evidence.
6. Limitations, assumptions, and failure modes.
7. How this paper influenced later AI/ML/Deep Learning/GenAI work.
8. A 30-minute study plan with checkpoints.
9. Quiz me with 5 questions and wait for my answers.

When something is not available in the attached context, say what is missing and infer carefully.

### Paper attached as context
Title: An Overview of Trustworthy AI: Advances in IP Protection, Privacy-Preserving Federated Learning, Security Verification, and GAI Safety Alignment
Authors: Yue Zheng, Chip-Hong Chang, Shih-Hsu Huang, Pin-Yu Chen, S. Picek
Year: 2024
Venue: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Categories: AI Safety, Foundation Models, Large Language Models, Popular and Landmark Papers
Citations: 24
Paper URL: https://www.semanticscholar.org/paper/9fd0f21867d6acf7048b8acbb3f04e85b50e6e52
Open PDF: Not available

Abstract:
AI has undergone a remarkable evolution journey marked by groundbreaking milestones. Like any powerful tool, it can be turned into a weapon for devastation in the wrong hands. Understanding that no model is perfect, trustworthy AI is initiated with an intuitive aim to mitigate the harm it can inflict on people and society by prioritizing socially responsible AI ideation, design, development, and deployment towards effecting positive changes. The scope of trustworthy AI is encompassing, covering qualities such as safety, security, privacy, transparency, explainability, fairness, impartiality, robustness, reliability, and accountability. This overview paper anchors on recent advances in four research hotspots of trustworthy AI with compelling and challenging security, privacy, and safety issues. The topics discussed include the intellectual property protection of deep learning and generative models, the trustworthiness of federated learning, verification and testing tools of AI systems, and the safety alignment of generative AI systems. Through this comprehensive review, we aim to provide readers with an overview of the most up-to-date research problems and solutions. By presenting the rapidly evolving factors and constraints that motivate the emerging attack and defense strategies throughout the AI life-cycle, we hope to inspire more research effort into guiding AI technologies towards beneficial purposes with greater robustness against malicious use intent.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=An%20Overview%20of%20Trustworthy%20AI%3A%20Advances%20in%20IP%20Protection%2C%20Privacy-Preserving%20Federated%20Learning%2C%20Security%20Verification%2C%20and%20GAI%20Safety%20Alignment)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=An%20Overview%20of%20Trustworthy%20AI%3A%20Advances%20in%20IP%20Protection%2C%20Privacy-Preserving%20Federated%20Learning%2C%20Security%20Verification%2C%20and%20GAI%20Safety%20Alignment)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/9fd0f21867d6acf7048b8acbb3f04e85b50e6e52)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=An%20Overview%20of%20Trustworthy%20AI%3A%20Advances%20in%20IP%20Protection%2C%20Privacy-Preserving%20Federated%20Learning%2C%20Security%20Verification%2C%20and%20GAI%20Safety%20Alignment+paper+explained)