TITLE: Seeing the Unseen: Errors and Bias in Visual Datasets 

ABSTRACT: From face recognition in smartphones to automatic routing on self-driving
cars, machine vision algorithms lie in the core of these features. These
systems solve image based tasks by identifying and understanding objects,
subsequently making decisions from these information. However, errors in
datasets are usually induced or even magnified in algorithms, at times
resulting in issues such as recognising black people as gorillas and
misrepresenting ethnicities in search results. This paper tracks the errors in
datasets and their impacts, revealing that a flawed dataset could be a result
of limited categories, incomprehensive sourcing and poor classification.
From face recognition in smartphones to automatic routing on self-driving
cars, machine vision algorithms lie in the core of these features. These
systems solve image based tasks by identifying and understanding objects,
subsequently making decisions from these information. However, errors in
datasets are usually induced or even magnified in algorithms, at times
resulting in issues such as recognising black people as gorillas and
misrepresenting ethnicities in search results. This paper tracks the errors in
datasets and their impacts, revealing that a flawed dataset could be a result
of limited categories, incomprehensive sourcing and poor classification.
Seeing the Unseen: Errors and Bias in Visual Datasets
http://arxiv.org/abs/2211.01847v1