Graduation Year
2025
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
School of Information Systems and Management
Major Professor
Shivendu Shivendu, Ph.D.
Committee Member
Balaji Padmanabha, Ph.D.
Committee Member
Manish Agrawal, Ph.D.
Committee Member
Mark Bender, Ph.D.
Keywords
AI Bias, Digital Divide, Game Theory, Public Policy
Abstract
The impact of Information technology on human lives is beyond measure. Information technology has become so integral to our human lives that we can no longer undermine its societal impact. While there is ample evidence for the societal gains from digital technologies, there is evidence for the inequitable distribution of such gains across society. For example, accelerated digitization of economy has left the digital haven-nots with limited access to education, health care and employment opportunities further deepening the already existing social inequities. Furthermore, increasing implementation of algorithms has created the problem of algorithmic bias across multiple domains including hiring. Though it was expected that the lack of human intervention in the sourcing phase of algorithmic hiring would eliminate discrimination against protected population groups, recent research has shown evidence for algorithmic bias in the sourcing phase of algorithmic hiring. Thus, it is evident that the gains from advancements in Information systems such as Information and Communication Technologies and Artificial Intelligence have not reached certain sections of society. These inequities in the distribution of benefits of Information Systems can be resolved with government intervention in the form of subsidies and regulations. The goal of this dissertation is the investigation of policy initiatives in technology as an approach to address these issues using game theoretical approach. The first chapter focuses on optimal subsidy policy to bridge the digital divide gap in the presence of digital literacy training investments by digital platforms. The second chapter focuses on the optimal regulation policy to mitigate sourcing bias in job-matching platforms. The third chapter focuses on the optimal government mechanism to procure social goods in the presence of demand uncertainty.
Scholar Commons Citation
Syed, Roohid Ahmed, "Essays on Policy Initiatives for Equitable and Fair Distribution of Benefits of Information Technology" (2025). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/11013
