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Transparency and Accountability
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Author(s): Princy Pappachan (Department of Foreign Languages and Literature, Asia University, Taiwan), Massoud Moslehpour (Department of Business Administration, Asia University, Taiwan & Department of Management, California State University, San Bernardino, USA), Ritika Bansal (Insights2Techinfo, India)and Mosiur Rahaman (International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan)
Copyright: 2024
Pages: 34
Source title:
Challenges in Large Language Model Development and AI Ethics
Source Author(s)/Editor(s): Brij Gupta (Asia University, Taichung City, Taiwan)
DOI: 10.4018/979-8-3693-3860-5.ch006
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Abstract
The rapid growth and application of AI has ushered in ground-breaking technologies like LLMs. However, these innovations also bring significant challenges related to transparency and accountability, especially considering the complex neural network architectures and vast training datasets. This chapter thus explores the journey of AI from rule-based systems to the current ML and deep neural network, identifying the black box problem that plagues the decision-making process in LLMs. The chapter introduces strategies for enhancing transparency using explainable AI (XAI) frameworks to address these issues, offering practical solutions to quantify and improve transparency. Accountability is also emphasized through a detailed protocol for assigning responsibility across AI development phases, reinforced by ethical auditing and reporting methodologies. Mathematical equations and frameworks are also presented to compute transparency scores and accountability measures, providing organizations with structured, actionable guidelines for building transparent, fair, and ethical AI systems.
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