Dataset diversity: measuring and mitigating geographical bias
in image search and retrieval
Mandal, Abhishek, Leavy, Susan and Little, SuzanneORCID: 0000-0003-3281-3471
(2021)
Dataset diversity: measuring and mitigating geographical bias
in image search and retrieval.
In: 1st International Workshop on Trustworthy AI for Multimedia Computing, 24 Oct 2021, Chengdu, China.
ISBN 978-1-4503-8674-6
Many popular visual datasets used to train deep neural networks
for computer vision applications, especially for facial analytics,
are created by retrieving images from the internet. Search engines
are often used to perform this task. However, due to localisation
and personalisation of search results by the search engines along
with the image indexing method used by these search engines, the
resultant images overrepresent the demographics of the region from
where they were queried from. As most of the visual datasets are
created in western countries, they tend to have a western centric
bias and when these datasets are used to train deep neural networks,
they tend to inherit these biases. Researchers studying the issue of
bias in visual datasets have focused on the racial aspect of these
biases. We approach this from a geographical perspective. In this
paper, we 1) study how linguistic variations in search queries and
geographical variations in the querying region affect the social and
cultural aspects of retrieved images focusing on facial analytics, 2)
explore how geographical bias in image search and retrieval can
cause racial, cultural and stereotypical bias in visual datasets and
3) propose methods to mitigate such biases.
Trustworthy AI'21: Proceedings of the 1st International Workshop on Trustworthy AI for Multimedia Computing.
.
Association for Computing Machinery (ACM). ISBN 978-1-4503-8674-6