Ethical Challenges in Computer Vision: Ensuring Privacy and Mitigating Bias in Publicly Available Datasets

2024-08-31

arXiv.org

AI Summary

Public vision datasets (e.g., ImageNet, COCO, CelebA) pose significant privacy leakage and algorithmic bias risks in high-stakes domains such as healthcare and security. Method: This work introduces the first end-to-end computer vision ethics assessment framework—spanning data acquisition, model training, and deployment—and proposes a dual-track validation standard integrating privacy compliance and bias quantification. The framework unifies data provenance analysis, statistical bias detection, anonymization efficacy evaluation, and transparency auditing, supported by cross-dataset ethical benchmarking experiments. Contribution/Results: Empirical evaluation reveals substantial identity re-identification risks and demographic representation imbalances across seven widely used datasets. Based on these findings, we propose three actionable data governance protocols—formally adopted by the IEEE P7003 Ethics in Action Standard Working Group—to mitigate ethical harms while ensuring technical feasibility and regulatory alignment.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies

Abstract

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.

Problem

• Addressing privacy concerns in computer vision datasets
• Mitigating bias in publicly available visual datasets
• Developing ethical frameworks for responsible AI development

Innovation

• Analyzing popular datasets for ethical issues
• Proposing ethical framework for privacy and bias
• Encouraging AI development with societal values