Page 374 - Kaleidoscope Academic Conference Proceedings 2024

2024 ITU Kaleidoscope Academic Conference

communication. Studies have explored AI's role in 1. Count of Indian generative AI startups (2021-May 2023)
streamlining regulatory compliance [6] and improving e-
governance service delivery and decision-making [6]. 2. Total funding of Indian generative AI startups in USD
Retrieval-Augmented Generation (RAG) [7] improves the millions (2021-May 2023)
accuracy of AI-generated content by integrating information
retrieval. RAG has shown promise in synthesizing legislative 3. Global preferences for generative AI usage across sectors
documents for informed governance [8] and enhancing (2023)
responsiveness and cost-effectiveness, particularly in
resource-constrained settings like India [9]. However, AI 4. Indian CISOs' views on generative AI adoption (May-July
adoption in governance faces challenges related to 2023)
organizational readiness, infrastructure, trust, and workforce
capabilities [10]. Ethical concerns surrounding privacy, bias, 5. Drivers of increased generative AI usage in various
and transparency have also been highlighted [11]. AI- countries (2023)
powered platforms can facilitate dynamic government- The inclusion of CISOs' perspectives is particularly relevant
citizen communication [11] and personalize public services
to improve satisfaction [6]. AI can automate routine tasks, as they have a deep understanding of the security
freeing resources for complex activities in governance [12]. implications and potential risks associated with AI adoption
These studies underscore the importance of a holistic in governance systems. Their insights contribute to a more
approach to AI adoption, leveraging the complementary holistic assessment of the readiness and feasibility of
strengths of different technologies to create more robust and integrating Generative AI and RAG technologies in the
effective governance systems. While the existing literature Indian e-governance context. The study complies with all
provides valuable insights into the potential of generative AI relevant guidelines and regulations governing data use,
and RAG in transforming governance, there is a need for strengthening the reliability and validity of the findings [16].
To comprehensively address the research question and
more context-specific studies that explore the unique
challenges and opportunities of AI adoption in developing objectives, the study employs a suite of advanced data
countries like India, where e-governance is still an evolving analysis techniques, each meticulously selected for its
paradigm. As AI continues to advance and become more relevance and applicability to specific aspects of the
integrated into governance processes, it is crucial to develop investigation. These techniques include trend analysis
robust frameworks and guidelines that promote transparency, (datasets 1 and 2) to identify AI startup growth patterns and
accountability, and fairness, especially in the context of investment trajectories, predictive modeling (dataset 4) to
citizen-centric governance. forecast AI's potential benefits across sectors, sentiment and
text analysis (dataset 4) to gauge perceptions and attitudes
towards AI adoption in governance, factor analysis (dataset
3. RESEARCH METHODOLOGY
5) to identify underlying factors driving AI usage in
The research design for proposed study encompasses a governance, and cluster analysis (datasets 3 and 5) to
multi-faceted strategy, leveraging diverse data, advanced segment data into clusters of similar responses or usage
analytical techniques, and robust methodological scenarios. Each technique contributes methodologically by
considerations to address the research objectives effectively. offering novel perspectives, uncovering latent variables, and
The study relies on both primary and secondary data to enabling targeted interventions for AI integration in e-
provide a comprehensive understanding of the AI landscape governance. The study employs descriptive and inferential
in India's e-governance context. Primary data is collected statistics, with data preprocessing to ensure quality and
through semi-structured interviews with key stakeholders, comparability. Analytical techniques are selected based on
including government officials, AI experts, and citizen data and research questions, using Python, Pandas, Scikit-
representatives. These interviews offer valuable insights into Learn, and Matplotlib and other relevant data science
the challenges, opportunities, and perceptions surrounding libraries [17]. Cross-validation and sensitivity analyses
the integration of Generative AI and RAG technologies in e- assess model performance and result robustness. Ethical
governance. Secondary data is sourced from Statista, a standards, data anonymization, and research integrity are
reputable provider of market and consumer data [15]. The prioritized, acknowledging limitations and biases.
datasets, spanning from 2021 to 2023, offer a wealth of
quantitative insights into the growth trajectory, funding 4. DATA ANALYSIS
landscape, public perception, and adoption drivers of
generative AI technologies in India's e-governance context. The data analysis section of this research offers a
The research employs five datasets: comprehensive and systematic exploration of the integration

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