Harnessing AI For Inclusive Security: Addressing Challenges In Deaf-Dominant Areas

Abstract

This study investigates how artificial intelligence (AI) might improve security for Deaf people by removing barriers to communication in emergency scenarios. Security is particularly difficult for deaf populations because traditional institutions are built for hearing people, which leaves them exposed in emergency situations. Barriers to communication, such the use of vocal warnings and aural alerts, increase these hazards and emphasise the need for more inclusive solutions. How can AI enhance security for Deaf people? is the main topic that the study seeks to address. Incorporating AI-driven technologies such as real-time translation, sign language recognition, and predictive analytics, this study suggests a more effective and accessible security framework for Deaf communities.  To collect information on current security issues and AI applications, the study uses questionnaires, interviews, and case studies as part of an exploratory research approach. The main technologies covered include real-time translation systems that translate spoken language into text or sign language, predictive analytics to anticipate possible threats and speed up security response times, and sign language recognition, which enables Deaf people to interact with emergency responders. The study also points out weaknesses in the security solutions that are currently in place, most notably the incompatibility of AI technologies with the infrastructure that is already in place. The case studies' results demonstrate how well AI-powered solutions may increase accessibility and user satisfaction, despite ongoing integration and accuracy issues. The study adds to the expanding corpus of research on inclusive security by providing useful suggestions for the creation and application of AI solutions that take into account the particular requirements of Deaf people. Subsequent investigations ought to concentrate on improving these technologies and tackling moral dilemmas in their application.