A survey on bias in visual datasets

Computer Vision and Image Understanding(2022)

引用 29|浏览37
暂无评分
摘要
Computer Vision (CV) has achieved remarkable results, outperforming humans in several tasks. Nonetheless, it may result in significant discrimination if not handled properly. Indeed, CV systems highly depend on training datasets and can learn and amplify biases that such datasets may carry. Thus, the problem of understanding and discovering bias in visual datasets is of utmost importance; yet, it has not been studied in a systematic way to date. Hence, this work aims to: (i) describe the different kinds of bias that may manifest in visual datasets; (ii) review the literature on methods for bias discovery and quantification in visual datasets; (iii) discuss existing attempts to collect visual datasets in a bias-aware manner. A key conclusion of our study is that the problem of bias discovery and quantification in visual datasets is still open, and there is room for improvement in terms of both methods and the range of biases that can be addressed. Moreover, there is no such thing as a bias-free dataset, so scientists and practitioners must become aware of the biases in their datasets and make them explicit. To this end, we propose a checklist to spot different types of bias during visual dataset collection.
更多
查看译文
关键词
41A05,41A10,65D05,65D17
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要