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

Source: [Ethical Challenges in Computer Vision: Ensuring Privacy and Mitigating Bias in Publicly Available Datasets](<https://www.sota2.com/research/paper/68fa7a8a1e209131cea83cc3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:59:41.376Z

Authors: Ghalib Ahmed Tahir

Published: 2024-08-31T00:59:29.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2409.10533>)

[PDF](<https://arxiv.org/pdf/2409.10533.pdf>)