Authors

Venue

Date

Share

A Pathway Towards Responsible AI Generated Content

Lingjuan Lyu

IJCAI 2023

2023

Abstract

AI Generated Content (AIGC) has received tremendous attention within the past few years, with content ranging from image, text, to audio, video, etc. Meanwhile, AIGC has become a double-edged sword and recently received much criticism regarding its responsible usage. In this article, we focus on three main concerns that may hinder the healthy development and deployment of AIGC in practice, including risks from privacy; bias, toxicity, misinformation; and intellectual property (IP). By documenting known and potential risks, as well as any possible misuse scenarios of AIGC, the aim is to sound the alarm of potential risks and misuse, help society to eliminate obstacles, and promote the more ethical and secure deployment of AIGC.

Related Publications

FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low- Rank Adaptations

NeurIPS, 2024
Lingjuan Lyu, Ziyao Wang, Zheyu Shen, Yexiao He, Guoheng Sun, Hongyi Wang, Ang Li

The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Federated learning (FL) further enhances fine-tuning in a privacy-aware manner by utilizing clients'…

pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning

NeurIPS, 2024
Jiaqi Wang*, Lingjuan Lyu, Fenglong Ma*, Qi Li

Federated learning, a pioneering paradigm, enables collaborative model training without exposing users’ data to central servers. Most existing federated learning systems necessitate uniform model structures across all clients, restricting their practicality. Several methods …

CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence

NeurIPS, 2024
Chaochao Chen*, Yizhao Zhang*, Lingjuan Lyu, Yuyuan Li*, Jiaming Zhang, Li Zhang, Biao Gong, Chenggang Yan

With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models. Machine unlearning has emerged as a potential solution to enable selective forgetting in model…

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.