Transparency and Accountability

Author:

Pappachan Princy1ORCID,Moslehpour Massoud2ORCID,Bansal Ritika3,Rahaman Mosiur4

Affiliation:

1. Department of Foreign Languages and Literature, Asia University, Taiwan

2. Department of Business Administration, Asia University, Taiwan & Department of Management, California State University, San Bernardino, USA

3. Insights2Techinfo, India

4. International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan

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.

Publisher

IGI Global

Reference43 articles.

1. Bergner, B., Skliar, A., Royer, A., Blankevoort, T., Asano, Y., & Bejnordi, B. E. (2024). Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding. arXiv preprint arXiv:2402.16844.

2. Benchmarking and survey of explanation methods for black box models

3. Brown, N. B. (2024). Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs. arXiv preprint arXiv:2406.01943.

4. The black box problem was revisited. Real and imaginary challenges for automated legal decision making.;B.Brożek;Artificial Intelligence and Law,2023

5. Accountable Artificial Intelligence: Holding Algorithms to Account

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