在稳定扩散文本到图像模型中研究毒性和偏差
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在PubMed上查看摘要
概括
此摘要是机器生成的。像"稳定扩散"这样的流行的文字图像模式会产生有害的内容, 包括暴力和偏见的图像, 随着人工智能图像的普及, 迫切需要采取安全措施.
科学领域
- 人工智能
- 计算机视觉
- 人工智能道德
背景情况
- 文本到图像模型正在迅速发展,引发了严重的安全性和公平性问题.
- 人工智能图像生成越来越容易获得,因此需要对潜在的危害进行彻底评估.
研究的目的
- 调查流行的稳定扩散模型产生有害内容的倾向.
- 根据有害提示,识别人工智能生成的图像中的偏差.
主要方法
- 使用有害提示的十个稳定扩散模型的评估.
- 对性,暴力和敏感内容的生成图像进行分析.
- 对模型拒绝行为和安全机制的评估.
主要成果
- 模特产生的内容不合适, 包括性和暴力图像.
- 观察到明显的偏见,比如在暴力环境中不成比例地描绘黑人.
- 在评估的模型中没有检测到拒绝行为或安全措施.
结论
- 目前的稳定扩散模型缺乏基本的安全功能,造成风险.
- 解决偏见和实施安全协议对于负责任的AI开发至关重要.
- 人工智能图像生成的广泛整合需要迫切关注道德考虑.
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