剖析视觉数据集的隐私与偏见问题，提出伦理框架促公正AI发展
Ethical Challenges in Computer Vision: Ensuring Privacy and Mitigating Bias in Publicly Available Datasets
- 分析COCO、ImageNet等主流数据集的伦理风险
- 揭示无授权采集图像引发的隐私泄露与偏见放大问题
- 适合关注AI伦理、数据安全的研究者与开发者
本文聚焦计算机视觉技术在使用公开数据集时面临的伦理挑战。随着机器学习与人工智能的快速发展，计算机视觉已广泛应用于医疗、安防、商业等领域。然而，大量视觉数据在未经知情同意的情况下被采集和使用，引发严重的隐私担忧与偏见问题。本文以COCO、LFW、ImageNet、CelebA、PASCAL VOC等常用数据集为例，系统分析其潜在伦理风险。提出一个涵盖个体权利保护、偏见最小化及开放责任的综合性伦理框架，旨在推动人工智能开发兼顾社会价值与伦理标准，避免对公众造成伤害。
原文摘要 · Abstract (English)
This paper aims to shed light on the ethical problems of creating and deploying computer vision tech, particularly in using publicly available datasets. Due to the rapid growth of machine learning and artificial intelligence, computer vision has become a vital tool in many industries, including medical care, security systems, and trade. However, extensive use of visual data that is often collected without consent due to an informed discussion of its ramifications raises significant concerns about privacy and bias. The paper also examines these issues by analyzing popular datasets such as COCO, LFW, ImageNet, CelebA, PASCAL VOC, etc., that are usually used for training computer vision models. We offer a comprehensive ethical framework that addresses these challenges regarding the protection of individual rights, minimization of bias as well as openness and responsibility. We aim to encourage AI development that will take into account societal values as well as ethical standards to avoid any public harm.
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