Composite Artificial Intelligence

6Removing Bias in Maritime Imagery: Advancing Gender Equality through Data-Driven Methods

Jordan Taylor¹ and J. Padmapriya^2*

¹Project Harrison, California, USA

²AMET University, Chennai, India

Abstract

The maritime industry exhibits a pronounced gender imbalance, with women comprising one to two percent of the global seafaring workforce. Despite international efforts to advance gender equality, underrepresentation persists. As a result, female seafarers face significant taxation as a result of unequal working conditions, namely social and psychological challenges at sea and ashore. Further, women encounter entry barriers such as limited access to decision-making roles, training programs, and a lack of female role models. Cultural norms and traditions in some countries can serve to exacerbate gender disparity by normalizing gender inequality. The United Nations 2030 Agenda for Sustainable Development is one of many global efforts to promote gender equality by creating fair workplaces. To enhance gender inclusivity, systemic policy reforms are required, including the introduction of mentorship programs and targeted recruitment initiatives, as advocated by organizations such as the International Maritime Organization. We posit that (1) innovative technologies such as computer vision can serve to quantify and visualize systemic challenges in gender equity and (2) that many existing vision models exhibit difficulty in identifying women that don personal protective equipment in ...