Evaluating Gender-Neutral Training Data for Automated Image Captioning: Reducing Bias in AI

Evaluating Gender-Neutral Training Data for Automated Image Captioning

Authors: Jack J Amend, Albatool Wazzan, Richard Souvenir

Conference: International Conference on Big Data, 2021

This paper explores the crucial role of training data in shaping the performance and fairness of automated image captioning systems. As AI applications become increasingly pervasive, addressing potential biases is paramount. The authors delve into the impact of using gender-neutral training data on the accuracy and fairness of image captioning models.

Key areas of focus include:

• The importance of identifying and mitigating gender bias in AI.* Methodologies for creating and evaluating gender-neutral training datasets.* The impact of gender-neutral data on the performance of image captioning models. * Strategies for promoting fairness and inclusivity in automated image captioning applications.

This research contributes to the growing body of knowledge on mitigating bias in AI and highlights the importance of carefully considering training data to ensure the development of fair and ethical AI systems.