[Paper Review] A Pathway Towards Responsible AI Generated Content

Chen Chen, Jie Fu+1 more|arXiv (Cornell University)|Mar 2, 2023

Artificial Intelligence in Healthcare and Education23 citations

TL;DR

This paper surveys 8 key risks of AI Generated Content (AIGC) and outlines directions to develop responsible AIGC by addressing privacy, bias, IP, robustness, open source, abuse, consent/credit, and environmental concerns.

ABSTRACT

AI Generated Content (AIGC) has received tremendous attention within the past few years, with content generated in the format of image, text, 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 8 main concerns that may hinder the healthy development and deployment of AIGC in practice, including risks from (1) privacy; (2) bias, toxicity, misinformation; (3) intellectual property (IP); (4) robustness; (5) open source and explanation; (6) technology abuse; (7) consent, credit, and compensation; (8) environment. Additionally, we provide insights into the promising directions for tackling these risks while constructing generative models, enabling AIGC to be used more responsibly to truly benefit society.

Motivation & Objective

• Identify the eight main concerns hindering responsible AIGC deployment (privacy, bias/toxicity/misinformation, IP, robustness, open source and explainability, technology abuse, consent/credit/compensation, environment).
• Provide insights and directions for mitigating these risks in the construction and deployment of generative models.
• Discuss how foundation models enable AIGC and how risks propagate across modalities (text, image, video, audio).

Proposed method

• Review and synthesize existing literature and industry practices on AIGC risks.
• Map risk categories to concrete mitigation strategies (data curation, filtering, watermarking, access controls, governance).
• Propose discussion of policy, technical, and societal approaches to responsible AIGC across lifecycle stages.
• Highlight representative models and datasets to illustrate risk areas (privacy leakage, dataset biases, memorization, IP concerns).

Figure 1: The scope of responsible AIGC. Note that some icons are from Shutterstock.

Experimental results

Research questions

• RQ1 What are the principal risks associated with AI Generated Content across privacy, bias/toxicity/misinformation, IP, robustness, open source, abuse, consent/credit, and environment?
• RQ2 What directions and strategies can be pursued to mitigate these risks while enabling beneficial uses of AIGC?
• RQ3 How do foundation models contribute to risks and how can mitigation be integrated into model design and deployment?

Key findings

• AIGC faces interconnected risks spanning privacy, bias, misinformation, IP, robustness, openness, abuse, and environmental impact.
• Mitigation approaches include data filtering, deduplication, watermarking, output filtering, model recalibration, and governance mechanisms.
• Content ownership and IP attribution remain legally unresolved, prompting practices like DMCA takedown policies, watermarking, and attribution considerations.
• Hallucinations and misinformation stem from training data quality, overfitting, and prompt design; regular data updates and user feedback can help reduce them.
• Open-source transparency is debated; while openness aids explanation, it also raises risks of misuse and competitive concerns.
• There is a need for governance, consent, and compensation models so data contributors can benefit from AIGC training data.
• Environmental costs of large models motivate exploring slimmer models and efficiency-focused research.

Figure 2: A comparison between training images and generated images (by Stable Diffusion). Top row : generated images. Bottom row : closest matches in the training dataset (LAION). The comparison shows that Stable Diffusion is able to replicate training data by combining foreground and background ob