Ethical and Societal Ramifications in Human-AI use Patterns
Open Access
- Author:
- Liu, Sharon
- Area of Honors:
- Computer Science
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Christopher Dancy, Thesis Supervisor
Mohamed Khaled Almekkawy, Thesis Honors Advisor - Keywords:
- artificial intelligence
disaster management
flood response
social equity
marginalized communities
ethical frameworks
artificial intelligence
disaster management
flood response
social equity
marginalized communities
ethical frameworks
Human-AI interaction - Abstract:
- This study proposes a novel framework for integrating human-AI interactions in flood disaster response that specifically addresses the exclusion of marginalized and racialized communities. Through a semi-systematic review of literature from 2018 to 2024, utilizing the PRISM method to analyze 20 relevant frameworks, this research identifies significant gaps in current disaster management systems where vulnerable populations are frequently overlooked. The proposed Framework for AI-Inclusive Response in Flooding (FAIR-F) consists of four pillars: Ethical Integration, Interactive Response, Community Centric Governance, and Data Rights and Protection. These pillars collectively address systemic inequalities while maintaining operational efficiency in flood response efforts within Pennsylvania. The framework synthesizes elements from existing disaster management protocols with Critical Race Theory principles and health equity values to create a hybrid model that embeds social equity considerations into technical response protocols. While the framework shows promise in addressing historical biases in disaster response, validation and testing of FAIR-F needs to be conducted in order to determine its effectiveness. This research contributes to the growing body of work on ethical AI implementation in disaster management and provides a foundation for future studies on equitable emergency response systems.