This paper is published in Volume-10, Issue-4, 2024
Area
Design
Author
Advik Shukla
Org/Univ
Pathways School , Gurgaon, India
Keywords
Gender Bias, Flawed Technology, Women Safety, Stereotypes, Biased Data, Inclusive Design.
Citations
IEEE
Advik Shukla. Inherent Gender Bias in Women Safety And LLMs with Special Emphasis on Engineering Design Defects and Technological Aspects, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Advik Shukla (2024). Inherent Gender Bias in Women Safety And LLMs with Special Emphasis on Engineering Design Defects and Technological Aspects. International Journal of Advance Research, Ideas and Innovations in Technology, 10(4) www.IJARIIT.com.
MLA
Advik Shukla. "Inherent Gender Bias in Women Safety And LLMs with Special Emphasis on Engineering Design Defects and Technological Aspects." International Journal of Advance Research, Ideas and Innovations in Technology 10.4 (2024). www.IJARIIT.com.
Advik Shukla. Inherent Gender Bias in Women Safety And LLMs with Special Emphasis on Engineering Design Defects and Technological Aspects, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Advik Shukla (2024). Inherent Gender Bias in Women Safety And LLMs with Special Emphasis on Engineering Design Defects and Technological Aspects. International Journal of Advance Research, Ideas and Innovations in Technology, 10(4) www.IJARIIT.com.
MLA
Advik Shukla. "Inherent Gender Bias in Women Safety And LLMs with Special Emphasis on Engineering Design Defects and Technological Aspects." International Journal of Advance Research, Ideas and Innovations in Technology 10.4 (2024). www.IJARIIT.com.
Abstract
Current safety and technology systems often overlook the unique needs of women. Issues with crash test dummies, stab vests, CPR techniques, and AI systems reveal significant risks and discomfort for women due to designs based primarily on male standards.Addressing these issues requires integrating female-specific models in safety tests, improving the design of protective gear, and correcting biases in AI systems. Implementing these changes will ensure that both men and women receive fair and effective protection, making technology and safety measures more equitable and reliable for everyone.
