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Optimizing Interpretability and Dataset Bias in Modern AI Systems
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Author(s): L. K. Hema (Department of ECE, Vinayaka Mission.s Research Foundation (DU), Aarupadai Veedu Institute of Technology, India), Rajat Kumar Dwibedi (Department of ECE, Vinayaka Mission.s Research Foundation (DU), Aarupadai Veedu Institute of Technology, India), Muppala Deepak Varma (Department of ECE, Vinayaka Mission.s Research Foundation (DU), Aarupadai Veedu Institute of Technology, India), Anamika Reang (Department of ECE, Vinayaka Mission.s Research Foundation (DU), Aarupadai Veedu Institute of Technology, India), S. Silvia Priscila (Bharath Institute of Higher Education and Research, India)and A. Chitra (Dharmamurthi Rao Bahadur Calavala Cunnan Chetty's Hindu College, India)
Copyright: 2024
Pages: 19
Source title:
Cross-Industry AI Applications
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5951-8.ch009
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Abstract
As AI systems become deeply ingrained in societal infrastructures, the need to comprehend their decision-making processes and address potential biases becomes increasingly urgent. This chapter takes a critical approach to the issues of interpretability and dataset bias in contemporary AI systems. The authors thoroughly dissect the implications of these issues and their potential impact on end-users. The chapter presents mitigative strategies, informed by extensive research, to build AI systems that are not only fairer but also more transparent, ensuring equitable service for diverse populations. Interpretability and dataset bias are critical aspects of AI systems, particularly in high-stakes applications like healthcare, criminal justice, and finance. In the study, the authors delve deep into the challenges associated with interpreting the decisions made by complex AI models.
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