Creating an Inclusivity Index for Digital Twin (IIDT) Transportation Systems - An Interdisciplinary Research

Research Project, 2025 – 2026

A digital twin in the context of AI and transportation systems refers to a digital replica that includes both the data and its semantics used to simulate the real world. This replica enables the development and validation of AI systems that make automated decisions in real-world scenarios. The importance of ensuring inclusivity in the digital twin is crucial for AI development. If the digital twin fails to accurately represent all population groups, particularly those that are often underrepresented such as people with disabilities, the elderly, or those with other mobility issues, the resulting AI systems will inherit these gaps. This can lead to biases in automated decision-making, potentially endangering certain groups or failing to meet their specific needs. Therefore, it is important to ensure that the digital twin includes a comprehensive and diverse set of data reflecting the full spectrum of society to enable AI systems to function effectively and equitably in the real world. In addition, developing and incorporating indexes to assess the diversity and inclusiveness of digital twins is highly beneficial, promoting the development of more accurate and fair AI systems.