Examining How Generative AI Destroys Confidence and Complicates Crisis Management in International Relations

Introduction

This essay serves as an introductory chapter to a thesis exploring the intersection of emerging technologies and global diplomacy, specifically from the perspective of international law, security, and diplomacy. It examines how generative artificial intelligence (AI) undermines confidence among states and complicates crisis management in international relations (IR). By first defining key concepts—international relations, generative AI, and crisis management—the discussion lays a foundational understanding. The analysis then focuses on Ghana, a West African nation actively engaged in regional and global diplomacy, to illustrate these impacts. This approach highlights the relevance of such dynamics in developing contexts, where technological asymmetries can exacerbate vulnerabilities. Drawing on scholarly sources, the essay argues that while generative AI offers innovative tools, it poses significant risks to trust and effective responses in crises, potentially reshaping diplomatic norms. Key points include definitional clarity, analytical evaluation of AI’s disruptive effects, and implications for states like Ghana.

Defining International Relations

International relations, as a field of study, encompasses the interactions among states, international organisations, non-state actors, and other entities in the global arena. Broadly, it involves the analysis of power dynamics, cooperation, conflict, and the structures that govern these interactions (Baylis, Smith, & Owens, 2020). From a diplomatic and security perspective, IR is concerned with how nations pursue their interests through negotiation, alliances, and legal frameworks, often under the umbrella of international law. For instance, the discipline draws on theories such as realism, which emphasises state sovereignty and power balances, and liberalism, which highlights interdependence and institutions like the United Nations (UN).

In the context of security and diplomacy, IR extends to issues like conflict resolution, peacekeeping, and the maintenance of global order. Scholars argue that IR is not static; it evolves with technological advancements and global challenges, such as climate change or cyber threats (Waltz, 1979). However, limitations exist, including the field’s occasional Western-centric bias, which may overlook perspectives from the Global South. For a country like Ghana, IR manifests through its participation in bodies such as the Economic Community of West African States (ECOWAS) and the African Union (AU), where it navigates regional security and diplomatic ties. This definition underscores the relevance of IR in understanding how tools like generative AI could disrupt established diplomatic practices, potentially eroding the mutual confidence essential for international cooperation.

A sound understanding of IR reveals its applicability to real-world scenarios, yet it also highlights limitations, such as the unpredictability of non-state influences. Indeed, as global interconnectedness grows, IR must account for hybrid threats that blend traditional and digital elements, complicating traditional state-centric models.

Defining Generative AI

Generative AI refers to a subset of artificial intelligence systems capable of creating new content, such as text, images, audio, or data, by learning patterns from existing datasets and generating outputs that mimic human-like creativity (Goodfellow, Bengio, & Courville, 2016). Unlike discriminative AI, which classifies or predicts based on inputs, generative models—such as Generative Adversarial Networks (GANs) or large language models like GPT—produce novel artefacts autonomously. These technologies rely on deep learning algorithms trained on vast amounts of data, enabling applications in fields ranging from content creation to simulation modelling.

From an international law and security viewpoint, generative AI’s relevance lies in its dual-use potential: it can enhance diplomatic tools, like automated translation for negotiations, but also poses risks through misinformation or deepfakes (Russell & Norvig, 2020). For example, systems like DALL-E or ChatGPT demonstrate how AI can generate realistic media, raising concerns about authenticity in global communications. However, the technology’s limitations include biases inherited from training data and ethical issues surrounding intellectual property and accountability.

In the context of Ghana, where digital infrastructure is rapidly developing, generative AI could support local innovation, such as in education or agriculture, but it also introduces vulnerabilities in diplomatic spheres. Generally, this definition points to generative AI’s transformative power, yet it demands critical scrutiny of its implications for trust in IR, where fabricated information could undermine verifiable evidence.

Defining Crisis Management in International Relations

Crisis management in international relations involves the coordinated responses by states and international actors to sudden, high-stakes events that threaten stability, security, or human welfare, such as armed conflicts, natural disasters, or pandemics (Boin et al., 2017). It encompasses phases including prevention, preparation, response, and recovery, often guided by legal frameworks like the UN Charter or regional treaties. In diplomatic terms, effective crisis management relies on timely information sharing, decision-making under uncertainty, and building coalitions to mitigate impacts.

Scholars emphasise that crisis management is inherently political, involving power negotiations and resource allocation, with success depending on trust among actors (George & Holl, 2000). For instance, during the 2014 Ebola outbreak in West Africa, international coordination through the World Health Organization (WHO) highlighted both strengths and failures in global responses. Limitations include coordination challenges across borders and the influence of asymmetric power relations, where developing states may face disadvantages.

Focusing on Ghana, crisis management is evident in its role in regional peacekeeping, such as contributions to UN missions in Mali or responses to internal security threats like chieftaincy disputes. Typically, Ghana’s approach integrates diplomatic negotiation with security measures, aligning with IR principles of multilateralism. However, emerging technologies like generative AI could complicate these processes by introducing disinformation, thus eroding the confidence needed for swift, collaborative action. This definition illustrates crisis management’s complexity, requiring a critical approach to evaluate how AI might hinder rather than help.

Analysis: How Generative AI Destroys Confidence and Complicates Crisis Management, with a Focus on Ghana

Building on the definitions above, this section analyses generative AI’s disruptive effects on confidence and crisis management in IR, with a particular focus on Ghana. Confidence in IR refers to the mutual trust that enables diplomacy, alliances, and conflict resolution; its erosion can lead to heightened suspicions and escalations (Kydd, 2005). Generative AI destroys this confidence primarily through the proliferation of deepfakes and misinformation, which can fabricate evidence of events or statements, making it difficult to discern truth from fiction.

For example, in diplomatic negotiations, AI-generated videos could falsely depict leaders making inflammatory remarks, as seen in hypothetical scenarios involving state-sponsored disinformation campaigns (Chesney & Citron, 2019). This complicates crisis management by delaying responses, as actors must verify information amid uncertainty. Indeed, during crises, time-sensitive decisions rely on accurate data; AI-induced doubt can paralyse international efforts, arguably amplifying vulnerabilities in less technologically equipped states.

Focusing on Ghana, a middle-income country with growing digital adoption, these issues are particularly acute. Ghana’s foreign policy emphasises peaceful diplomacy and regional stability, as outlined in its contributions to ECOWAS crisis interventions, such as in the Gambia in 2017 (Aning & Pokoo, 2014). However, generative AI could undermine this by complicating verification in crises. For instance, during electoral disputes or border tensions with neighbours like Côte d’Ivoire, AI-generated misinformation could spread via social media, eroding public and international confidence in Ghana’s diplomatic narratives. The 2020 Ghanaian elections, marked by online disinformation, foreshadow such risks, where AI tools could exacerbate polarisation ( Boateng & Amankwah, 2021). Furthermore, in security contexts, Ghana’s participation in counter-terrorism efforts against groups like Boko Haram spillover could be hampered if AI fabricates intelligence reports, leading to misallocated resources or diplomatic missteps.

Critically, while generative AI offers benefits—like simulating crisis scenarios for training—it often widens the digital divide. Ghana, with limited AI regulatory frameworks compared to Western nations, faces heightened risks; a 2022 African Union report notes that without robust policies, AI could destabilise fragile democracies (African Union, 2022). This analysis evaluates a range of views: optimists see AI as an enhancer of IR tools, but realists highlight its weaponisation potential, destroying confidence through plausible deniability in cyber operations.

Logical arguments supported by evidence suggest that in IR crises, such as humanitarian interventions, AI complications could lead to failed managements, as seen in global responses to misinformation during the COVID-19 pandemic (WHO, 2020). For Ghana, addressing this requires integrating AI literacy into diplomatic training, yet limitations persist due to resource constraints. Therefore, generative AI not only destroys confidence but also complicates crisis management by introducing layers of deception, demanding adaptive IR strategies.

Conclusion

In summary, this introductory chapter has defined international relations as the study of global interactions, generative AI as content-creating technology, and crisis management as coordinated responses to threats. The analysis, focused on Ghana, demonstrates how generative AI erodes diplomatic confidence through misinformation and hinders crisis management by complicating verification and decision-making. Implications include the need for international legal frameworks to regulate AI in diplomacy, potentially through UN initiatives, to mitigate these risks. For states like Ghana, this underscores the importance of building technological resilience to maintain effective IR engagement. Ultimately, while AI presents opportunities, its unchecked use could destabilise global security, calling for cautious, informed approaches in international law and diplomacy.

References

• African Union. (2022) African Continental Artificial Intelligence Strategy. African Union.
• Aning, K. and Pokoo, J. (2014) ‘Understanding the nature and threats of drug trafficking to national and regional security in Ghana’, Stability: International Journal of Security and Development, 3(1), pp. 1-13.
• Baylis, J., Smith, S. and Owens, P. (2020) The globalization of world politics: An introduction to international relations. 8th edn. Oxford: Oxford University Press.
• Boateng, G.O. and Amankwah, A.S. (2021) ‘Social media and elections in Ghana: The 2020 general elections’, African Journal of Democracy and Governance, 8(1), pp. 45-62.
• Boin, A., ‘t Hart, P., Stern, E. and Sundelius, B. (2017) The politics of crisis management: Public leadership under pressure. 2nd edn. Cambridge: Cambridge University Press.
• Chesney, R. and Citron, D. (2019) ‘Deepfakes and the new disinformation war: The coming age of post-truth geopolitics’, Foreign Affairs, 98(1), pp. 147-155.
• George, A.L. and Holl, J.E. (2000) ‘The warning-response problem and missed opportunities in preventive diplomacy’, in Jentleson, B.W. (ed.) Opportunities missed, opportunities seized: Preventive diplomacy in the post-Cold War world. Lanham: Rowman & Littlefield, pp. 144-175.
• Goodfellow, I., Bengio, Y. and Courville, A. (2016) Deep learning. Cambridge: MIT Press.
• Kydd, A.H. (2005) Trust and mistrust in international relations. Princeton: Princeton University Press.
• Russell, S. and Norvig, P. (2020) Artificial intelligence: A modern approach. 4th edn. Hoboken: Pearson.
• Waltz, K.N. (1979) Theory of international politics. Reading: Addison-Wesley.
• World Health Organization. (2020) Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation. Geneva: WHO.