Abstract
Artificial intelligence (AI) is poised to transform political science and international relations in the coming decades. This paper provides an overview of the current state of AI technology, explores potential applications in political analysis and governance, and considers ethical implications for democracy and world order. After reviewing technical capabilities, the analysis focuses on (1) computational political science and big data analytics, (2) automation in public administration, (3) simulation of complex social systems, and (4) AI augmentation and autonomy in foreign policymaking. While acknowledging risks, the paper argues that responsible development of AI can make government more effective, empirical social science more rigorous, and international relations more stable. However, preserving humanistic values and democratic accountability should remain central concerns as societies navigate the transition.
Introduction
From deep learning neural networks beating humans at poker and Go, to AlphaFold determining protein structures, artificial intelligence (AI) has rapidly advanced in the past decade (Silver et al. 2016; Callaway 2020). While narrow AI focuses on specific tasks, ongoing progress hints at the eventual creation of artificial general intelligence (AGI) with more open-ended capabilities (Kaplan and Haenlein 2019). As AI comes to match or surpass human intellectual abilities over the next several decades, it could transform political science and international relations in momentous ways (Cave and ÓhÉigeartaigh 2018; Dafoe 2018). This paper analyzes the current state of AI and potential implications for researchers and practitioners in the fields of political science, international relations, public administration, and foreign policy.
The analysis is organized into four main sections following this introduction. The first section reviews the technical landscape of AI today – including machine learning, neural networks, and capabilities for analyzing large datasets – while noting progress and limitations. The second section considers applications to political science research and domestic governance, such as using AI for computational analysis or public sector automation. The third section discusses international implications, including simulating global systems, augmenting foreign policymaking, and debating the effects on world order. The fourth section examines normative concerns and proposals for responsible AI development that upholds democratic values. The conclusion summarizes key arguments and reflects on balanced optimism for AI’s potential in political science and international relations.
Current State of Artificial Intelligence
Artificial intelligence commonly denotes “the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity” (Tegmark 2017, 48). While AI has gone through cycles of optimism and disappointment known as “AI winters”, capabilities have substantially advanced since the 1980s (Markoff 2015). This progress is driven by exponential growth in computing power, vast data accumulation, and algorithmic advances – especially in machine learning (Brynjolfsson and McAfee 2014; Agrawal et al. 2018). AI systems cannot yet match generalized human cognition, but exceed human performance on an increasing number of specific tasks.
Machine Learning and Neural Networks
Much recent progress in AI is powered by machine learning, where algorithms improve through exposure to data without the need for explicit programming (Jordan and Mitchell 2015). Learning tasks include pattern recognition, function approximation, prediction, and optimization. Common methods include supervised learning by recognizing patterns in labeled training data, unsupervised learning to find hidden structures, and reinforcement learning guided by rewards or punishments. Deep learning uses artificial neural networks modeled on the brain’s interconnected neurons and synapses. Multiple layers detect hierarchical features and patterns in data, which enables capabilities like image and speech recognition (LeCun et al. 2015). For example, an object recognition model may connect pixels to edges, edges to shapes, shapes to object parts, and parts to holistic objects. Neural networks can process unstructured data like images, text, and audio – going beyond information in neatly tabular form.
Capabilities and Limitations
Tasks currently within reach of AI include games like chess and Go, autonomously driving vehicles operating under certain constraints, transcribing speech and translating between languages, detecting spam and fraud, placing relevant ads, diagnosing medical conditions, and generating news stories or creating art based on learned patterns (Kaplan and Haenlein 2019; Brynjolfsson and McAfee 2014). However, despite impressive capabilities, contemporary AI still pales in comparison to generalized human cognition. While trained systems can exceed humans on particular well-defined tasks, they remain narrow or “weak” AI focused on niche applications rather than broad contexts (Searle 1980; Brooks 2017). For example, the AlphaGo system that defeated a world champion at Go could not switch tasks and play chess at any level without completely retraining. AI agents also lack capacities for robust common sense reasoning, explaining their inferences, and transferring knowledge between contexts. Current machine learning methods require extensive data to train high-performing models, whereas people can learn new concepts from little experience. Some experts argue contemporary techniques will hit limits soon and that progressing further toward broad AI capabilities matching humans will require more fundamental advances (Gary and Russell 2021). However, the field remains in its relative infancy decades before experts project the potential creation of strong AI.
AI in Political Science and Public Policy
As AI technologies continue advancing in the coming years and decades, they are poised to transform political science research and domestic governance in major ways. This section surveys promising applications including computational social science, public sector automation, modeling social systems, and algorithmic decision-making. It reviews how AI can improve knowledge and policy, while noting risks and challenges to manage.
Computational Social Science
AI provides powerful new tools for political scientists to analyze data about political behavior (Lazer et al. 2009). The internet and social media generate vast datasets on society in real time. Web scraping can accumulate information at scales impossible to gather manually. Machine learning techniques are effective for pattern recognition, classification, and prediction across large and messier types of data than traditional statistical methods can easily manage. For example, natural language processing facilitates analyzing text sources like political speeches, legislation, manifestos, election coverage, and social media. Computer vision can help interpret visual content like political ads, posters, and protest footage. Neural networks uncover latent structures and relationships across many variables.agent-based models can simulate macro dynamics emerging from individual interactions (Cederman 2005). This “computational social science” paradigm lets researchers test theories with more real-world representativeness and granularity (Lazer et al. 2009). AI can complement qualitative methods and human judgment, while discovering novel hypotheses for further study.
However, analysts should be aware of potential biases encoded in training data or algorithms when applying AI (O’Neil 2016). Models extrapolating from current patterns may miss political shocks and discontinuities. Overemphasis on prediction risks neglecting explanation and theory. Still, with proper caution, AI expands social scientists’ toolkit to uncover new insights at greater scope. It also creates opportunities to make analysis more cumulative, transparent, and reproducible across the field (King 1995). Overall, responsible use of AI can make political science more empirically rigorous.
Automating Governance
AI is also poised to take over many administrative and analytical tasks within government. In public services, chatbot assistants can answer constituent questions or take applications (Mehr 2017). Back-office document processing can be automated to improve efficiency. AI can help match policies to citizen priorities expressed in surveys, social media, or counseling sessions (Fukuyama 2021). It can optimize logistics like trash collection routes or ambulance dispatching. Algorithms can assist judges in sentencing by providing risk assessments and precedent analysis, though appropriate human oversight is critical. AI augmentation may enable small states to “punch above their weight” with limited civil service capacity (Crootof 2020). However, governments must ensure accountability and transparency when deploying opaque algorithms, to uphold procedural values (Zerilli et al. 2019).
Within the policy process, AI systems can rapidly synthesize research to identify evidence-based options, or model complex systems to simulate potential outcomes. This could improve policy analysis capacity where human capital is scarce (Crootof 2020). But when tools become black boxes, overreliance risks undermining deliberative governance. International collaboration sharing best practices, standards, and monitoring will be valuable for guiding integration of automation in the public sector. Overall, AI can enhance government effectiveness and empirical grounding. But risks of bias, opacity, and de-skilling should be managed through governance frameworks ensuring human oversight and responsibility (Fjeld et al. 2020).
Modeling Social Systems
AI also creates new capabilities to model social and political complexity. Agent-based models can simulate millions of adaptive actors interacting in dynamic nonlinear systems (Cederman 2005). This can test how macro-level dynamics like ethnic conflict may emerge from individual behaviors and dispositions. AI can train generative adversarial networks, where two neural nets compete to respectively generate realistic examples and judge authenticity. This approach could generate artificial societies with realistic complexity, which social scientists can perturb to study counterfactuals (Stachurski 2021). AI can run many experimental simulations faster, larger, and more empirically grounded than humans can roleplay. However, care is needed when training models, as biased data or algorithms could reproduce social injustices. Verifying insights against theory and evidence remains vital. With proper caution, AI-assisted simulation and generative modeling create new potentials for social science to study complexity, contingency, and causality. But human judgment must still interpret what dynamics, observations, and interventions matter.
Algorithmic Governance and Automation
Some theorists propose automating decision-making itself by transferring policy to algorithms trained to optimize predefined social welfare functions (Russell 2019). However, this risks removing human values, judgment, and accountability from the policy process. It could also empower the programmers defining ostensibly “neutral” optimization goals (Hacker 2018). Automated decision systems may neglect cultural nuance and struggled in novel situations requirements general common sense lacking in contemporary AI (Brynjolfsson and McAfee 2014). Perhaps some narrowly defined bureaucratic functions could transfer to algorithms with oversight. But for most high-stakes public policy matters, human discretion and responsibility should remain central even if AI systems inform decisions. Fully automated technocratic governance risks priority social values of participation, deliberation, and self-determination (Frey and Gallus 2018). More prudently, political scientists can use AI augmentation to provide knowledge and capabilities supporting human decision-makers, who stay ultimately accountable to democratic publics. With responsible implementation, AI analytics and modeling offer valuable resources for governance while avoiding risks of excessive automation.
International Relations and Foreign Policy
In international affairs, AI will likewise transform empirical analysis along with diplomacy and strategy. This section reviews potential applications in global systems modeling, foreign policy decision support systems, and autonomous weapons. It considers impacts on interstate dynamics and debates over AI’s net effects on world order and stability.
Modeling International Systems
Many questions in international relations concern how system-level structures shape state interactions (Waltz 1979). What global dynamics emerge from military rivalries, economic ties, or information networks between nations? AI opens new possibilities to simulate these complex, adaptive systems. Agent based models can represent diverse state actors along many dimensions – geography, demographics, culture, regime type, resources, alliances etc – and test how overall patterns develop from their interactions (Cederman 2005). Machine learning can uncover tendencies and causal logics in rich datasets like militarized disputes, trade flows, diplomacy exchanges, or cyber operations. With enough computational power and data, AI could potentially reproduce complex dynamics between states that match real world evidence and provide laboratories for theory testing.
However, inherent uncertainty, contingencies, and novelties in global affairs pose challenges for AI prediction (Frey 2019). Models extrapolating from past data may miss future outliers and unknown unknowns. AI could still assist international relations scholarship with knowledge discovery, scenario planning, and uncertainty mapping – shedding light where human analysis struggles with complex causality across many variables. But human judgment must contextualize insights for wise application. For policymakers, AI assessment can supplement deliberation without automating high-stakes decisions on matters like sanctions, alliances, or uses of force. Responsibly applied to model complexity and contingencies, AI can enrich understanding in international relations, even if unpredictabilities remain.
AI in Foreign Policy and National Security
Within government, AI analytics have growing applications in foreign policy and national security (Allen and Husain 2017). As in domestic affairs, AI can assist document processing, information retrieval, and administrative functions. More ambitiously, some propose AI decision support systems to help policymakers evaluate options and likely impacts. For instance, during crises AI could rapidly gather intelligence, model scenarios, assess legal factors, predict global reactions, and recommend responses – automating the “OODA loop” of observation, orientation, decision, and action (Allen and Chan 2017). However, foreign policy judgment requires broader understanding of context, values, and qualitative uncertainties beyond AI’s capabilities (Horowitz et al. 2018). Perhaps machine learning tools could someday obtain sufficient training data to reliably advise on a narrow spectrum of recurrent, codifiable dilemmas. But for novel, ambiguous foreign policy challenges, human strategists likely remain indispensable.
Autonomous Weapons
Military applications also raise controversial possibilities of autonomous weapons systems – from cyber defense tools to drones and robot soldiers with lethal force authority (Ekelhof 2019). Advocates claim AI could react faster in combat or enforce legal standards of proportionality and distinction. Critics counter it may dangerously erode human control, emotions like empathy, and martial honor codes (Crootof 2016). full autonomation risks severing the moral relationship between soldiers sacrificing lives and the public bearing responsibility. However, considering permissive standards under international law, autonomous capabilities appear likely to expand (Geiss 2015). Carefully crafted legal limits and human oversight will remain important for responsible development. Compared to automation in domestic governance, autonomous force poses unique risks from empowering machines over life and death. This underscores needs for ethical precautions and transparent public debate if democracies are to pursue such technologies.
Effects on World Order
What might be the net international impacts of AI advancement? Optimists envision benefits like reducing miscalculation through simulation and analysis, overcoming biases among human decision-makers, and tightening global integration (Mueller 2018). AI coordination could also enhance management of global public goods like climate change mitigation. However, risks remain that automation and autonomy fuel instability and arms races. Overconfident trust in algorithms could empower reckless state actions. Fully autonomous weapons could dangerously accelerate violence and confuse accountability. AI-enabled surveillance states may also undermine liberal values (Frey 2019). AI has potential to improve or imperil world order. But since impacts depend greatly on how humanity applies technologies, maintaining cooperation and democratic oversight of AI development will remain critical challenges for responsible statecraft in coming decades.
Governing Responsible AI Innovation
Given profound potential social impacts, ethically orienting AI technologies to promote human flourishing is essential (Dafoe 2018). Technical and policy communities increasingly recognize needs to proactively address risks through research and governance. This concluding section surveys proposals for upholding humanistic values as societies navigate the AI transition.
Values in AI Development
Computer science research on “AI safety” and “AI ethics” focuses on engineering systems that align with human values, retain meaningful oversight, and avoid unintended harms (Amodei et al. 2016). Approaches include value sensitive design, “AI constitutions” codifying principles, and techniques for explainable AI and human-machine collaboration (Winfield and Jirotka 2018). Legal scholars consider regulating development and uses of AI for accountability (Zerilli et al. 2021). Applied ethicists also deliberate principles and governance frameworks for morally guided innovation (Floridi et al. 2018). Continued multidisciplinary dialogue and collaboration will be essential to instill ethics within technical design and policy. Dialogue can also help cultivate public trust and democratic oversight for socially accepted applications of powerful technologies.
Protecting Rights and Freedoms
Law and regulation have roles upholding freedoms and rights as AI advances. Data privacy frameworks will grow more crucial as systems produce, analyze, and share more personal information. Preventing state surveillance overreach may require strengthening due process, transparency, and civilian oversight (Zerilli et al. 2021). Rules against manipulating voters via micro-targeted disinformation will be important for electoral integrity. Protections for workers displaced by automation and citizens reliant on government AI services should adapt social safety nets for the transition. International accords can help states cooperatively govern shared risks like cyber threats and autonomous weapons (Ekelhof 2019). Grounding AI governance in human rights law and democratic principles can help societies navigate tradeoffs and stay oriented towards protecting freedoms.
Institutions for Responsible Development
Specialized institutions may also steward responsible AI progress (Dafoe 2018). Proposed models include oversight boards within tech companies, non-governmental organizations supporting best practices, and government agencies conducting technology assessments. Independent algorithm auditing could verify properties like transparency, fairness, and security (Raji et al. 2020). AI monitoring agencies might survey systems for potential harms once deployed. “Red team” units could stress test AI safety and security experimentally. International organizations could facilitate experience sharing, standard setting, and policy coherence across countries (Cath 2018). Such institutional infrastructure can enhance societal capacities governing AI for public benefit. But ensuring good governance that earns legitimacy and adapts appropriately over time will remain an ongoing challenge.
Preserving Humanistic Governance
Most fundamentally, preserving humanistic values and democratic accountability should remain central priorities for AI governance (Floridi et al. 2018). Automating decisions on public concerns risks undermining dignity, participation, and self-determination. Therefore, policy processes and oversight should retain meaningful human leadership and discretion. AI systems can empower citizens and institutions with knowledge and tools, but should not prescribe governance from a technocratic remove. Even if advanced automation becomes technically feasible someday, retaining human values, ethics, and responsibilities in collective decision-making will remain vital for morally grounded societies. Preserving opportunities for human potential to flourish alongside technological progress should guide political communities through the AI transition.
Conclusion
Artificial intelligence will substantially transform political science, public administration, international relations and statecraft in the coming decades. AI offers empowering capabilities for data analysis, modeling complexity, informing decisions, and augmenting processes from welfare services to diplomacy. However, risks remain of opaque biases, overreliance on algorithms, dehumanizing automation, and misuse in warfare. Avoiding dystopian outcomes while achieving benefits will require responsible development and governance. Grounding innovation in human rights and democratic values can help societies navigate AI’s profound impacts. If guided prudently, artificial intelligence can become a valuable governing resource improving policy outcomes and rigorously informing social science. But preserving human oversight and wisdom remains imperative for morally centered statecraft and scholarship as humanity enters the algorithmic age.
References
Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2018. Prediction Machines: The Simple Economics of Artificial Intelligence. Boston: Harvard Business Press.
Allen, Gregory C. and Taniel Chan. 2017. “Artificial Intelligence and National Security.” Belfer Center for Science and International Affairs, Harvard Kennedy School.
