Conference Agenda

The paper provides an overview of how Artificial Intelligence (AI) is applied in different government sectors. Methodology is based on a systematic review of research papers retrieved from IBM, Web of Science and Scopus databases, NITI Aayog.

In today's digital transformation age, governments worldwide recognize the importance of seamless and efficient services for their citizens. With rapid technology advancements, digital experiences have become a vital aspect of government service delivery. By harnessing Generative AI, governments can enhance digital experiences, streamline service delivery, and meet evolving citizens' expectations. Enhanced use of Artificial Intelligence (AI) is increasingly becoming a focal point for governments. Using AI in Governance and public policy is an excellent opportunity for citizen engagement, accountability, and interoperability. It is also an opportunity for governments to increase efficiency in governance. The adoption of AI in various sectors of governance, such as healthcare, education, and agriculture, is helping to improve service delivery, enhance citizen engagement, and optimize resource utilization. However, there are also concerns about the potential risks and challenges associated with AI, such as privacy, bias, and accountability. Therefore, it is important for policymakers, regulators, and stakeholders to work together to develop a comprehensive framework for the ethical and responsible use of AI in governance, while also ensuring that it benefits all sections of society.

In the last decade, artificial intelligence has generated several challenges in societies, with a special focus on public administration. This paper discusses about the worldwide initiatives on AI and India’s current AI policy landscape, challenges in formulating AI policies, sector of relevance and challenges, AI use cases etc.

Artificial intelligence (AI) as a technology with human like intelligence capabilities is driving the public sector e-government reforms to unprecedented levels. AI innovations when adequately supported and financed, can offer more comprehensive, effective and efficient services like teaching and learning, research, community engagement and value addition in areas of decision making in Higher Education Institutions (HEI). A study to investigate the effect of AI Technologies awareness, students characteristics, Higher Education Institutions support mechanisms, AI legislation and Artificial Intelligence tensions was undertaken. Graduate students undertaking Information Technology programs and acquainted with AI technologies were purposively selected from two randomly selected public HEI in Uganda. Seventy two respondents in total participated in the study by filling a self-administered survey instrument. Validity of the instrument was ensured through solicitation of expert input to improve the consistency, accuracy and trustworthiness of the instrument as well as a factor analysis test which, established reliability. Regression analysis was conducted and findings indicate a positive significant relationship between students’ characteristics in terms of absorptive capacity, AI legislation and artificial intelligence tensions. Findings suggest that students who find it easy to seek, understand and adapt to information relating to AI technologies are likely to experience increased tensions related to AI. Secondly, the introduction or strengthening of AI legislation may not necessarily lead to a reduction in AI tensions which, results are contrary to conventional wisdom and or as one may intuitively expect or imagine. The paper recommends development of minimum industry standards, proactive engagement and collaboration with policy makers and other stakeholders, education and awareness, development of AI impact assessment to guide policymakers and the higher education sector to evaluate the potential risks and or consequences of adapting AI. Overall, this paper contributes to the ongoing discourse on the future of AI by reporting results which, challenge conventional wisdom and calls for increased focus on developing diverse strategies to support students in mitigating AI tensions in addition to exploring different regulatory approaches in integrating AI technologies. Future studies could also investigate comprehensive AI legislation frameworks for different stakeholders, the impact of social emotional learning and wellbeing on AI tensions among different stakeholders.

Key words: Artificial intelligence, artificial intelligence tensions, absorptive capacity, AI legislation

The "Right to Good administration", as enshrined in the Charter of Fundamental Rights of the European Union (the "Nice Charter"), aims to establish a new paradigm in the relationship between citizens and public administration. Article 41 of the Charter designates this right as a "fundamental right of the person," granting European citizens specific legal rights in their dealings with public institutions. This provision fosters closer interaction between citizens and EU institutions, as well as with the broader administrative apparatus.

This right includes a set of guarantees - such as transparency, impartiality, and administrative efficiency - that make it an effective tool for addressing contemporary legal and social challenges.

Over time, there has been a growing need, both at the EU and national levels, to modernize public administration by focusing on administrative efficiency and better addressing the needs of citizens. This right implies that public administrations should carefully interpret and respond to citizens' needs to build trust in the administrative system.

One of the primary goals in achieving this modernization is digitalization, which allows administrations to streamline processes and improve their relationship with citizens. In Italy, however, digitalization within public administration has encountered significant challenges, leading some to describe it as a "long march" toward full transformation. This difficulty stems

from both internal and external factors: internally, there is cultural resistance to innovation and a lack of adequate digital skills within the public administration; externally, legislative choices have often favored sector-specific interventions over a unified regulatory framework, complicating the implementation of coherent, efficient digital solutions.

Despite these challenges, digitalization is closely associated with "efficiency," as it enables faster administrative procedures, simplifies the interaction between administrations and citizens, and supports democratization and participation in public policy processes. Through digital transformation, administrations can automate processes, improve data sharing, and offer online services, thereby facilitating the Good Administration.

However, the digital transformation of public administration is not without its difficulties. It requires new skills, robust cybersecurity measures, and strategies to manage resistance to change within organizations.

In this context, it is relevant to examine the Italian National Recovery and Resilience Plan (PNRR) and its approach to digitalization. The Plan emphasizes the importance of equipping public administration with necessary digital tools and investing in digital skills and infrastructure to improve the efficiency and quality of services provided to citizens.

The Plan recognize the need for a citizen-centered administration, requiring a “new alphabet” for public administration from cultural and ethical perspectives. This approach emphasizes “administrative simplification,” linked to digital transition, and outlines essential modernization steps to create a responsive, efficient public administration for society.

In summary, the right to Good Administration, supported by digitalization, represents a crucial path for improving the functioning of public administration. By promoting transparency, effectiveness, and responsiveness, it aims to ensure that public institutions can better meet the demands of modern society and reinforce the citizens' trust in administrative bodies. This transformation, while challenging, has the potential to foster a more open, participative, and citizen-centered public administration.

*Introduction*

The state today has transformed into one of the biggest financial services providers (MoF, 2024). With BHIM, Rupay, public sector banks, and other state-led financial entities the government is providing financial services to millions of Indians both within and outside the country (Union Budget, 2024-25). In this context, we explore the risks that application of GenAI may pose if it is integrated in the system and offer potential solutions to mitigating these risks.

*Background*

Generative AI (hereon referred to as GenAI) is a large language model based on the transformer architecture (Vaswani et al. 2017). It differs from current AI models since it focuses on generating new data, rather than simply analyzing or classifying existing data (Banh and Strobel 2023). It employs machine learning algorithms to learn patterns and structures from a given dataset and uses this knowledge to create new, original content (Nie et al. 2024).

*Methodology*

We undertake a mixed methods approach to get an overview of the risks associated with GenAI in public financial services. First, we interview five financial managers and public servants working in the financial domain by reaching out to our network. Second, we peruse related literature to see relevant studies that highlight the risks of adopting GenAI in augmenting financial services. We use the methodology adopted by (Gioia, Corley, and Hamilton 2013) to draw themes of risks from our interviews and juxtapose these with the literature.

*Findings*

Our analysis of interviews and literature revealed the following twelve themes: accountability, compliance, data privacy, security, bias, fairness, transparency, interpretability, operational failures, reputational damage, quality control, and third-party dependency. These themes collectively highlight the complex challenges and risks associated with using generative AI in public financial services. To bind these GenAI risks within a theory, we utilize the Enterprise Risk Management (ERM) framework (Prewett and Terry 2018) which is a holistic approach to managing risks across an organization. These include four categories of risk:

1. Strategic risks: Accountability, Fairness, and Third-party dependency.

2. Operational Risks: Data privacy, Security, Operational failure, and Quality control.

3. Reporting Risks: Transparency and Interpretability.

4. Compliance Risks: Compliance and Reputational damage.

*Future Directions*

We offer three potential solutions for each of the four GenAI risks we enumerated above.

1. To manage strategic risks, public financial firms should focus on maintaining clear ownership of data, ensuring data equity, and build guidelines for ensuring vendor reliability.

2. To address operational risks, the state and public financial firms should prioritize data protection, fraud prevention, and reliability check on generated data.

3. To mitigate reporting risks, public financial organizations should focus on data transparency and data accuracy through the adoption of Explainable AI (XAI) methods.

4. To address compliance risks, financial institutions should ensure regular audits of model training data and use of XAI to ensure transparency in decisions based on GenAI models.

We believe by adopting these solutions public financial institutions would be able to offset GenAI risks and will be able to harness its potential in full measure.