Conference Agenda

The ongoing transformation of the external environment of Supreme Audit Institutions (SAIs)—driven by technological advancements, financial and economic crises, health pandemics, and political and social uncertainties—has reshaped citizen and stakeholder expectations regarding accountability and transparency in the management of public resources. In response, SAIs are under increasing pressure to invest in mechanisms that enhance the quality, impact, and relevance of their audits, thereby strengthening their essential role in safeguarding public goods. Furthermore, the use of this technology forms part of broader administrative reform and digital transformation strategies aimed at modernizing the public sector to become more efficient and responsive to contemporary challenges.

In this context, Artificial Intelligence (AI), particularly machine learning models, presents valuable opportunities for SAIs to improve the reliability and efficiency of audit processes. AI enables the analysis of large volumes of data, automation of tasks, predictive analytics, and pattern identification, which together contribute to more accurate detection of irregularities, cost reduction, and time savings. This allows auditors to focus on more analytical and decision-making activities. However, despite these opportunities, studies examining the application and implications of AI in public audit remain limited.

To address this gap, this study conducted qualitative exploratory research, focusing on two case studies: the Courts of Auditors of Brazil and Portugal. The research involved documentary analysis and in-depth interviews with auditors, IT specialists, and managers to understand perceptions and experiences regarding AI implementation in public audit.

Findings indicate that while AI offers significant potential, its effective adoption requires overcoming key challenges shared by both institutions. These include ensuring data quality, navigating regulatory frameworks, continuous employee training, fostering partnerships, and building institutional capacity. In this case, the successful implementation demands not only technological measures but also organizational initiatives that consider privacy, auditor reskilling, regulatory frameworks addressing data protection, AI ethics, and responsibility. Simplifying procurement processes and establishing strategic partnerships are also essential to facilitate the development of AI-based audit projects. This research also contributes by providing empirical evidence on how SAIs are approaching AI adoption in public Audit, highlighting practical challenges and organizational considerations that can inform future studies and policy development.

1.Problem Statement and Purpose

Accelerating the digital and intelligent transformation of cities is crucial for comprehensively enhancing urban capabilities and core competitiveness. Generative artificial intelligence presents a new opportunity for cities' digital and intelligent transformation. Accurately grasping the essence of urban digital-intelligent transformation is a necessary prerequisite for deeply understanding the role of generative artificial intelligence in this process. However, few studies have analyzed the role of generative artificial intelligence in urban digital and intelligent transformation from an ontological perspective.

This study aims to construct a cognitive framework for urban digital and intelligent transformation from an ontological perspective. By integrating theory with practice, it delves into the essence of such transformation, providing new perspectives and insights for clarifying the role of generative artificial intelligence in the digital and intelligent transformation of cities.

2. Methodology

Ontology provides this study with a scientific perspective. Ontology is the theory concerning existence, reality, and actuality. It shapes how humans understand the world and deepens their comprehension of how cognition influences action. Ontology reveals what kind of urban digital-intelligent transformation is desirable, necessary, and positive. The study then proposes a cognitive framework of "Organization-Operation-Technology" for urban digital-intelligent transformation from an ontological perspective, which is then elaborated in terms of three aspects: core elements, operational mechanisms, and dynamic evolution.The data collected through literature reviews, field surveys, and other methods were used to analyze the key characteristics of generative artificial intelligence technology and its specific contributions to enhancing the core competitiveness of urban areas. Additionally, the study highlights potential risks that warrant attention and proposes risk response strategies.

3. Findings

The study revealed that organization, operation, and technology are the core elements of urban digital-intelligent transformation. All theoretical and practical frameworks of urban digital-intelligent transformation are, in essence, creative integrations of these key components.

Urban digital-intelligent transformation faces a dual mission: enhancing digital-intelligent technological capabilities while mitigating its risks. The key to driving this transformation lies in empowering the "organization-operation" dual-wheel mechanism through digital-intelligent technologies.

The 'organization-operation-technology' framework constitutes a dynamic system. Digital-intelligent technologies actively shape and transform the 'organization-operation' state, where rational, appropriate, and beneficial elements are preserved and strengthened, while unsuitable or risk-prone components are continuously adjusted or discarded. This dynamic evolution of 'organization-operation' in turn generates new demands on the capabilities and scope of digital-intelligent technologies, driving both technological innovation and creative applications. This interaction manifests cyclical reinforcement and dynamic renewal characteristics.

4. Proposal

Government should enhance its efforts in five areas: division of labor, collaboration, technology, systems, and culture, to better leverage the role of generative AI in urban digital and intelligent transformation, thereby enhancing the people's sense of gain, happiness, and security, and modernizing the urban governance system and capacity for governance‌‌. First, refine governance division of labor to optimize organizational structures and operational processes. Secondly, pioneer new collaborative approaches to strengthen interconnections and shared governance. Third, strengthen technological awareness to balance functionality and risks. Fourth, improve the institutional framework to holistically balance development and security. Fifth, foster cultural identity to converge shared values and consensus.

5. References

Andrew Chadwick, Jennifer Stromer-Galley(2016).Digital Media, Power, and Democracy in Parties and Election Campaigns: Party Decline or Party Renewal? The International Journal of Press/Politics, 21(3), 283-293.Retrieved from:

https://librarysearch.royalholloway.ac.uk/discovery/fulldisplay/alma997679920202671/44ROY_INST:44ROY_VU2

Leva Mainardi.(2024). Change Management: Artificial Intelligence (AI) at the Service of Public Administrations. AI & SOCIETY, published online. Retrieved from:

https://link.springer.com/article/10.1007/s00146-024-02136-2

Margaret Stout. (2012). Competing Ontologies: A Primer for Public Administration. Public Administration Review,72(3), 388-398. Retrieved from:

https://doi.org/10.1111/j.1540-6210.2011.02530.x

I. PROBLEM STATEMENT AND PURPOSE

With the rapid development of Artificial Intelligence (AI) technology, it improves people's quality of life. However, in the process of rapid development of AI, there exists a large number of underserved populations that are neglected. Elderly groups are less capable of accepting and using smart devices and AI applications. Remote and rural areas are unable to access advanced AI services in a timely manner due to weak network infrastructure and lack of technical resources. Disabled people and low-income groups also face the problem of poor adaptability of AI services, which makes these groups marginalized in the AI era, further exacerbating social inequality and defeating the original purpose of AI development to benefit the public. The public sector should pay attention to the unfair behavior in AI development and make changes.

II.METHODOLOGY

Designed questionnaires for different underserved populations (the elderly, the disabled, residents in remote areas, etc.), covering their knowledge of AI technology, frequency of use, difficulties encountered in the process of use, and demand for AI services. Through the combination of online and offline methods, the scope of the survey sample was expanded to ensure the diversity and representativeness of the sample. Statistical methods were used to organize and analyze the recovered questionnaire data to draw quantitative conclusions.

III.FINDINGS

The survey shows that among the elderly population, few of them are able to skillfully use basic AI smart device applications, such as simple cell phone APPs, and most of them have operational difficulties when facing complex AI services. Among residents in remote areas, few of them have access to advanced AI education and medical services, mainly due to insufficient network coverage and lack of relevant equipment and professional guidance. Among people with disabilities, the voice interaction AI products that can be used by visually impaired people are less than the number of similar products in the market, and there are problems such as unclear voice prompts and complicated operation procedures; and it is almost difficult for the hearing impaired people to get effective information from the existing AI visual interaction services.

IV. PROPOSALS

The government should formulate and improve policies and regulations related to the fairness of AI services, and give policy support such as tax incentives and financial subsidies to AI enterprises that focus on underserved populations, so as to guide enterprises to increase their investment in this field. Establish a strict review mechanism for AI products and services to ensure that special groups can enjoy fair AI services. Strengthen public education to raise social awareness of the fairness of AI services, and advocate enterprises to fulfill their social responsibility and pay attention to the needs of underserved people. AI companies are encouraged to increase research and analysis of the needs of underserved populations during the product development phase, and to incorporate the usage needs of special groups into product design specifications.

1. Problem statement and purpose

The use of e-government has increasingly been recognised as a means of reforming and modernising the public sector, and providing modern public services that are responsive to citizen needs. The implementation of e-government is however, characterised by challenges, especially for developing countries. Developing countries, including Zambia, lag behind in e-government due to a myriad of issues that include unique environmental factors such as political stability, economy, legal framework and digital infrastructure. Many studies have been conducted around the world and conclusions drawn on the factors affecting e-government, however there is a lack of adequate literature that details what these general challenges imply for the different country contexts and to what extent they are affected by them. Research on e-government that focuses on country contexts is scarce, especially in Africa, and thus contextual understanding of challenges of e-government is limited.

This paper aims to analyse the factors affecting e-government policy and its implementation in Zambia’s government ministries and to analyse the role and influence of advocacy coalitions in the implementation of e-government in Zambia’s government ministries. By gaining a deeper understanding of the Zambian context and dynamics of the e-government policy subsystem, the study seeks to identify strategies for the accelerated development and implementation of e-government in Zambia’s government ministries.

2. Methodology

This is a qualitative study that adopted the multiple-case study method. Participants included ICT experts and officials from four government ministries and the Smart Zambia Institute (SZI). Purposive and snowball sampling was used to identify the study participants. Data was collected using semi-structured interviews. Inductive thematic analysis was used to identify the factors affecting e-government policy and its implementation. Additionally, underpinned by the Advocacy Coalition Framework, the study used both thematic and content analysis to analyse the role and influence of advocacy coalitions in e-government policy and its implementation.

3. Findings

The study identified several factors affecting e-government policy and its implementation and were categorised as; technological, organisational, policy and human factors. The study found both enabling and constraining factors. There were ongoing organisational changes aimed at enhancing efficiency and effectiveness in e-government. The challenges include inadequate financial resources, lack of collaboration among government ministries and lack of digital skills. In relation to the role and influence of coalition in e-government policy, the study finds that Zambia’s e-government policy subsystem is a unitary subsystem that is dominated by a single advocacy coalition. The coalition is comprised of high level political and administrative actors holding beliefs that e-government is a means for public sector reform and an enabler for economic growth.

4. Proposal

Zambia is a low-income, resource-constrained country. The recommendations for e-government development should thus be practical and reflective of this context. Devising mechanisms and programmes aimed at developing digital skills and shifting the digital culture among public servants, will build capacity to support e-government. Devising standard frameworks for inter-agency cooperation will enhance integrated governance, coordinated and streamlined e-service delivery and reduce duplication of efforts. Implementing digital literacy and skills development programmes for citizens alongside e-government awareness campaigns will help address low adoption rates. Standards and interoperability policies should be operationalised to ensure that new systems integrate with the GSB. These recommendations aim to build institutional, technical and human capacity to accelerate effective e-government implementation.