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Navigating Ethical Challenges of AI in Public Governance: Trust, Accountability, and Electoral Integrity

General Report August 18, 2025
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TABLE OF CONTENTS

  1. Current Trends in AI Adoption by Governments
  2. Core Ethical Risks in Public AI Systems
  3. Governance Frameworks for Trust and Accountability
  4. Protecting Electoral Integrity in the AI Era
  5. Conclusion

1. Summary

  • As of August 2025, the global landscape of artificial intelligence (AI) in public governance is rapidly evolving, presenting both opportunities and ethical challenges. Various governments have intensified their efforts to integrate AI technologies to streamline operations and enhance transparency. In the United States, the federal government has made significant strides in AI adoption across multiple agencies, including the Environmental Protection Agency, which effectively utilizes AI tools to align project assessments with pressing political priorities such as climate change. However, these advancements are accompanied by core ethical risks, including algorithmic bias, opacity in decision-making processes, and accountability gaps. The integration of AI poses critical challenges for fairness, equality, and individual privacy, particularly in the public sector where biased outcomes can have far-reaching consequences.

  • Ongoing discussions around AI governance frameworks underline the necessity of establishing regulations that promote accountability and public trust. In Southeast Asia, nations are emerging as key players in formulating ethical AI standards. As countries like Japan, the United States, and those within the EU compete with China, collaborative efforts, including workshops with ASEAN officials, are aimed at promoting human-centric AI policies. This trend is crucial, as Southeast Asia holds a unique position that influences AI governance on a global scale, especially in consideration of local perspectives and interests. Overall, the momentum generated in AI governance is foundational in addressing the inherent ethical dilemmas presented by these technologies.

  • Electoral integrity remains a pressing concern as AI continues to permeate democratic processes. The dual role of AI in enhancing election administration while also presenting risks, particularly from misinformation and manipulation tactics, necessitates multi-faceted safeguards and policy measures. As Bangladesh prepares for critical national and local elections, the implications of AI misuse highlight the urgency of developing comprehensive frameworks that not only protect electoral integrity but also enhance voter literacy. The collaborative involvement of civil society and technology experts is essential to establish moral guidelines for AI utilization in political campaigns, ensuring that democratic values are honored.

2. Current Trends in AI Adoption by Governments

  • 2-1. Scope of federal AI initiatives in the United States

  • In 2025, the U.S. federal government has significantly accelerated its adoption of artificial intelligence technologies across various agencies. Notably, AI integrations are aimed at enhancing operational efficiency, transparency, and decision-making processes. The Environmental Protection Agency (EPA) exemplifies this trend, utilizing AI tools, such as chatbots from companies like Anthropic, to streamline administrative functions and align project evaluations with urgent political priorities, such as climate change initiatives. This collaborative engagement between government bodies and AI providers demonstrates a strategic shift toward data-driven governance. However, while these advancements promise efficiency, they also raise critical discussions surrounding ethical governance and the need for robust oversight frameworks to mitigate potential risks like algorithmic bias and privacy infringements.

  • 2-2. Emerging AI tool deployments across agencies

  • The deployment of AI tools within federal agencies has become increasingly prominent, with a focus on automating administrative tasks and improving public service delivery. AI technologies are poised to revolutionize how government functions, allowing for quicker decision-making and enhanced program evaluations. Collaboration with AI providers, such as Anthropic, has allowed various agencies to access advanced tools at low or no cost, facilitating a more streamlined approach to managing public resources. The transformative impact is underscored by the integration of AI into crucial evaluations, suggesting a marked shift toward embracing technology as a pivotal factor in modern governance.

  • 2-3. Global leadership struggle in setting AI rules

  • As of mid-August 2025, a global contest for leadership in AI governance is emerging, particularly in Southeast Asia, where countries are vying to establish regulatory frameworks to guide ethical AI use. Japan, the United States, and Europe are competing with China to set international standards. Recently, workshops have been held to engage ASEAN officials in discussions about the OECD’s AI Policy Toolkit, which promotes a 'human-centric' approach to AI and aims to incorporate regional perspectives in shaping these rules. Japan’s efforts to advocate for these international standards underscore the critical importance of establishing reliable and transparent AI governance frameworks, particularly in an era of rapid technological advancement.

  • 2-4. Frontline role of Southeast Asia in governance debates

  • Southeast Asia is positioned as a key player in the ongoing governance debates surrounding AI. As major emerging economies, countries in this region, including Indonesia as a new BRICS member, are actively considering various frameworks for AI governance. The recent OECD workshops highlight Southeast Asia's unique role, as they seek to integrate local insights into international AI regulations. In contrast, China's influence through the BRICS framework further complicates the landscape. Participants of these discussions emphasize the need for AI governance structures that align with national interests while promoting reliability and ethical standards in technology usage. This critical engagement indicates that Southeast Asia will play a pivotal role in shaping the future of AI governance globally.

3. Core Ethical Risks in Public AI Systems

  • 3-1. Algorithmic bias and discrimination

  • Algorithmic bias represents one of the primary ethical concerns in public AI systems. AI algorithms are trained on historical data that may reflect existing societal biases, leading to discriminatory outcomes. For instance, there have been documented cases where AI systems used in hiring processes have favored candidates based on race, gender, or socioeconomic background due to the biased training data they were exposed to. This phenomenon raises critical questions about fairness and equality, especially in public sector applications where decisions may affect the lives and opportunities of individuals. To mitigate these biases, stakeholders are beginning to emphasize the importance of diverse training datasets and rigorous testing of AI systems to ensure equitable outputs.

  • 3-2. Opacity and lack of explainability

  • The opacity inherent in many AI systems complicates efforts to understand their decision-making processes. Many algorithms function as 'black boxes,' meaning they produce results without offering clear insights into how those results were achieved. This lack of transparency can undermine public trust, particularly when these systems are deployed in sensitive areas such as criminal justice or healthcare. As a response, there is a growing push for AI systems to incorporate explainable AI (XAI) principles, which aim to make AI decisions more interpretable and understandable to users. Ensuring that AI's workings are transparent can facilitate better accountability and foster trust among the public.

  • 3-3. Accountability gaps in automated decisions

  • Determining accountability in cases where AI systems cause harm or errors remains a significant challenge. When an automated system makes a decision that results in negative outcomes—be it financial losses, wrongful conviction, or inadequate healthcare—identifying who is responsible can be convoluted. This raises fundamental ethical questions about liability and accountability, especially in governance contexts. Current discussions emphasize the need for regulatory frameworks and clear guidelines to establish accountability frameworks that hold developers and organizations responsible for the actions of their AI systems, thus fostering a culture of ethical responsibility in AI deployment.

  • 3-4. Privacy and surveillance concerns

  • As AI systems often require extensive data collection to function effectively, concerns about privacy and surveillance have become paramount. Data collection practices can infringe upon individual privacy rights, especially when sensitive personal information is analyzed without adequate consent or transparency. Public discourse has increasingly highlighted the need for robust privacy regulations to protect citizens from potential overreach and misuse of their data. Efforts to balance technological advancement with privacy rights are underway, with many advocating for legislation that enforces data protection to safeguard individual privacy in AI applications.

  • 3-5. Misinformation and AI hallucinations

  • AI systems, particularly those involved in natural language processing, have shown tendencies to produce 'hallucinations'—instances where the model generates information that is incorrect or fabricated, yet presents it with apparent confidence. This challenge is not an isolated issue; studies suggest that hallucinations can occur upwards of 82% of the time in certain applications, particularly within legal frameworks. The real-world implications of such inaccuracies can be severe, leading to the dissemination of misinformation that undermines public trust in AI technologies. Consequently, there is an urgent need for developing oversight mechanisms and validation systems that can effectively mitigate the risks associated with AI-generated content, ensuring that AI systems are reliable and accountable.

4. Governance Frameworks for Trust and Accountability

  • 4-1. Principles of explainable AI governance

  • Explainable AI governance is fundamental in ensuring that AI systems operate in a transparent and accountable manner. As outlined in recent literature, the core principles of this governance framework focus on establishing clear standards that dictate how AI models are designed, deployed, and monitored. The objective is to create a governance structure that demystifies AI operations, allowing stakeholders to understand why decisions are made and the factors influencing those decisions. A successful implementation is particularly vital in high-stakes areas such as healthcare and finance, where the implications of AI decisions can significantly impact lives and livelihoods. One prominent framework emphasizes that effective explainable governance entails documenting model behavior, tracking performance metrics, and ensuring compliance with ethical and legal standards. Moreover, industry standards like the EU AI Act and NIST's AI Risk Management Framework further push the envelope on developing interpretability, which is crucial for fostering public trust. The growing use of generative AI—now reported at 71% among organizations—underscores the urgency of embedding governance into AI systems to replace opacity with clarity.

  • 4-2. Industry and government oversight models

  • Governance frameworks for AI necessitate a multi-faceted approach involving both industry standards and governmental oversight. As AI technologies proliferate, it has become essential for organizations to establish comprehensive governance structures that address the unique challenges posed by these systems. For instance, recent documents indicate that governments around the world are actively working to formalize regulations concerning AI usage, with the EU AI Act serving as a pivotal piece of legislation in this regard. These frameworks often encompass clear roles and responsibilities that delineate who is accountable for the ethical implementation of AI. Organizations are encouraged to create dedicated oversight bodies, such as Responsible AI Committees, which can provide guidance and monitor compliance with established ethical guidelines. The expectation is that as these regulatory measures evolve, they will facilitate better risk management and enable organizations to respond proactively to the ethical challenges that AI technologies present.

  • 4-3. Neuroscience-based SCARF approach for stakeholder buy-in

  • Incorporating psychological principles into AI governance frameworks is critical for successful stakeholder engagement and acceptance. The SCARF model—an acronym for Status, Certainty, Autonomy, Relatedness, and Fairness—provides a neuroscience-based perspective on how stakeholders interact with and perceive AI systems. This model highlights that emotional factors significantly influence the integration of AI into organizations, often creating resistance simply due to inherent psychological responses. Adapting the SCARF framework in AI governance allows organizations to build trust among teams and stakeholders by addressing concerns around autonomy and fairness in AI decision-making. It emphasizes transparency and the need for continuous communication, recognizing AI as integral collaborators rather than mere tools. This approach ensures that governance frameworks accommodate the human elements of AI integration, facilitating smoother transitions and greater acceptance of AI technologies.

  • 4-4. Building transparency through standardized processes

  • Establishing standardized processes is vital for building transparency within AI governance frameworks. Clear procedures not only enhance stakeholder confidence but also ensure that AI systems align with organizational values and regulatory mandates. As articulated in recent writings, effective governance involves defining protocols for data collection, model transparency, and algorithmic fairness. Organizations are urged to implement robust monitoring and auditing practices that facilitate accountability. They should regularly assess AI systems for bias and performance, maintaining open lines of communication with stakeholders to discuss findings and improvements. Such transparency ultimately reinforces trust, allowing organizations to not only comply with emerging regulatory standards but also cultivate a responsible approach to AI that prioritizes ethical considerations and social implications.

5. Protecting Electoral Integrity in the AI Era

  • 5-1. AI’s dual role in election administration

  • Artificial intelligence (AI) has rapidly emerged as a powerful tool in election administration, offering both significant benefits and notable risks. On one hand, AI can enhance the efficiency of electoral processes through improved data management, voter outreach, and real-time monitoring of election integrity. For instance, AI algorithms can optimize the logistics of polling station management, analyze voter feedback to enhance electoral engagements, and even automate aspects of vote counting to ensure accuracy. However, these advancements come with challenges that demand prudent oversight and governance to protect electoral integrity.

  • 5-2. Threats of deepfakes and automated misinformation

  • As Bangladesh approaches its national and local elections, the potential misuse of AI-generated deepfakes and automated misinformation presents a significant threat to the electoral process. Deepfakes—manipulated videos that depict individuals saying or doing things they never actually did—pose serious risks of misinformation that can mislead voters and incite conflict. The alarming ease with which such content can be created and disseminated heightens the potential for these tactics to be weaponized in a politically charged environment, where public trust in electoral systems is already fragile. Ensuring rigorous measures to identify and combat these threats is critical for maintaining the authenticity and credibility of the election.

  • 5-3. Safeguards and policy measures under consideration

  • To address these concerns, a multi-faceted approach to electoral integrity is under consideration. Key safeguards proposed include the establishment of electoral AI guidelines in partnership with civil society and technology experts, which would enforce ethical standards for AI use in political campaigns. The goal of these guidelines would be to demarcate acceptable uses of AI, outline penalties for misuse, and promote accountability across platforms. The creation of an electoral technology monitoring cell within the Election Commission is also suggested to proactively monitor the digital landscape for disinformation and coordinate rapid responses to AI-driven electoral manipulation. Overall, these measures seek to build resilience against the emerging threats posed by AI in the electoral landscape.

  • 5-4. Case focus: Bangladesh’s upcoming national and local polls

  • Bangladesh is on the brink of holding crucial national and local elections, and the implications of AI on these polls cannot be overstated. The country's regulatory institutions currently face challenges, including inadequate technological infrastructure and a lack of expertise to effectively manage the risks associated with AI manipulation. With the upcoming elections, there is an urgent need for comprehensive regulatory frameworks to ensure electoral integrity. Authorities and civil society must prioritize awareness initiatives, enhancing digital literacy among voters to recognize and resist misinformation. The international community also holds a vested interest in supporting Bangladesh as it navigates these complex challenges, ensuring that democratic values are upheld during this pivotal electoral moment.

Conclusion

  • The ongoing integration of AI into the public governance framework heralds significant advancements, yet it simultaneously introduces complex ethical dilemmas that must be addressed comprehensively. Issues related to algorithmic bias, lack of transparency, accountability voids, and privacy violations underscore the pressing need for robust governance frameworks that promote ethical AI use. As we advance in the electoral season, the urgency to safeguard democratic processes from potential AI-enabled manipulation is paramount. Policymakers are thus challenged to implement transparent oversight mechanisms, fund the development and deployment of explainable AI models, and foster international cooperation to establish cohesive regulatory frameworks.

  • Future initiatives must prioritize continuous monitoring and public engagement strategies, promoting a culture where AI is employed not only as a tool for enhancing efficiency but also as a guardian of democratic values. Stakeholders are encouraged to invest in public literacy programs that enhance understanding of AI's capabilities and limitations, ensuring communities are well-informed and resilient against manipulation tactics. The cooperative efforts between governmental bodies, civil society, and the international community will be crucial in navigating the complex challenges presented by AI in public governance, ultimately ensuring that AI advancements serve the greater public interest and uphold democratic principles throughout the world.

Glossary

  • AI Governance: AI governance refers to the frameworks and regulations established to oversee the ethical use and deployment of artificial intelligence technologies. This includes ensuring accountability, transparency, and adherence to ethical standards, particularly in areas affecting public welfare and trust.
  • Algorithmic Bias: Algorithmic bias occurs when AI systems deliver unfair outcomes due to prejudices in the training data or the design of the algorithms themselves. This can lead to discriminatory practices in various sectors, such as hiring or law enforcement, affecting marginalized groups disproportionately.
  • Explainable AI (XAI): Explainable AI refers to methods and techniques in AI development that make the operations and decisions of algorithms understandable to humans. The aim is to improve transparency and trust in AI systems, especially in critical applications like healthcare and criminal justice.
  • Deepfakes: Deepfakes are synthetic media created using AI that can manipulate videos and audio to make it appear as if individuals are saying or doing something they did not. This technology poses significant risks for misinformation and can undermine trust in media and democratic processes, particularly in elections.
  • Hallucinations (in AI): In AI, hallucinations refer to instances where a model generates information that is incorrect or wholly fabricated while presenting it confidently. This issue is pertinent in natural language processing applications, highlighting the need for validation mechanisms to ensure accuracy and reliability.
  • Electoral Integrity: Electoral integrity encompasses the principles and practices ensuring that elections are conducted fairly, transparently, and accountably. In the context of AI, maintaining electoral integrity involves guarding against manipulation and misinformation that could skew public trust and democratic outcomes.
  • Transparency in AI: Transparency in AI denotes the clarity with which algorithms operate and make decisions. This principle is essential for fostering trust among users and stakeholders, ensuring that AI systems are scrutinized and held accountable for their outputs and decisions.
  • Privacy Regulations: Privacy regulations are laws and guidelines designed to protect individuals' personal data from misuse and ensure that organizations handling such data operate transparently and ethically. In the context of AI, these regulations are critical to maintain user trust and compliance with legal standards.
  • Governance Frameworks: Governance frameworks for AI are structured approaches that define procedures, roles, and responsibilities for managing AI technologies within organizations. These frameworks are designed to mitigate risks, ensure ethical compliance, and establish accountability in AI deployments.
  • Infrastructure for Digital Governance: Infrastructure for digital governance refers to the technological and regulatory foundations necessary for effectively managing AI technologies in public administration. This includes the systems, policies, and capabilities required to oversee AI's implementation and its impacts on governance.
  • SCARF Model: The SCARF model is a framework in neuroscience that outlines five domains (Status, Certainty, Autonomy, Relatedness, and Fairness) affecting social interactions and behaviors. It is used in AI governance to understand stakeholder engagement and improve acceptance of AI technologies.

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