International Relations

Permanent URI for this collectionhttps://hdl.handle.net/10679/714

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Browsing by Institution Author "ÜNVER, Hamid Akın"

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    Democratization, state capacity and developmental correlates of international artificial intelligence trade
    (Taylor & Francis, 2023) Ünver, Hamid Akın; Ertan, A. S.; International Relations; ÜNVER, Hamid Akın
    Does acquiring artificial intelligence (AI) technologies from the US or China render countries more authoritarian or technologically less advantageous? In this article, we explore to what extent importing AI/high-tech from the US and/or China goes parallel with importers’ (a) democratization or autocratization, (b) state capacity, and (c) technological progress across a decade (2010–2020). Our work demonstrates that not only are Chinese AI/high-tech exports not congruous with importers’ democratic backsliding, but autocratization attributed to Chinese AI is also visible in importers of US AI. In addition, for most indicators, we do not observe any significant effect of acquiring AI from the US or China on importers’ state capacity or technological progress across the same period. Instead, we find that the story has a global inequality dimension as Chinese exports are clustered around countries with a lower GDP per capita, whereas US high-technology exports are clustered around relatively wealthier states with slightly weaker capacity over territorial control. Overall, the article empirically demonstrates the limitations of some of the prevalent policy discourses surrounding the global diffusion of AI and its contribution to democratization, state capacity, and technological development of importer nations.
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    ArticlePublicationOpen Access
    Securitization of disinformation in NATO’s lexicon: A computational text analysis
    (Center for Foreign Policy and Peace Research, İhsan Doğramacı Peace Foundation, 2022-07) Ünver, Hamid Akın; Kurnaz, A.; International Relations; ÜNVER, Hamid Akın
    Following the Russian meddling in the 2016 US elections, disinformation and fake news became popular terms to help generate domestic awareness against foreign information operations globally. Today, a large number of politicians, diplomats, and civil society leaders identify disinformation and fake news as primary problems in both domestic and foreign policy contexts. But how do security institutions define disinformation and fake news in foreign and security policies, and how do their securitization strategies change over years? Using computational methods, this article explores 238,452 tweets from official NATO and affiliated accounts, as well as more than 2,000 NATO texts, news statements, and publications since January 2014, presenting an unsupervised structural topic model (stm) analysis to investigate the main thematic and discursive contexts of these texts. The study finds that NATO’s threat discourse and securitization strategies are heavily influenced by the US’ political lexicon, and that the organization’s word choice changes based on their likelihood of mobilizing alliance resources and cohesion. In addition, the study suggests that the recent disinformation agenda is, in fact, a continuity of NATO’s long-standing Russiafocused securitization strategy and their attempt to mobilize the Baltic states and Poland in support of NATO’s mission.
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    Sources of ai innovation: More than a U.S.-China rivalry
    (Transatlantic Policy Quarterly, 2023) Ünver, Hamid Akın; Feldstein, S.; International Relations; ÜNVER, Hamid Akın
    Many experts frame the debates around AI technology as a great power rivalry between the U.S. and China. Indeed, by most measures, the United States and China lead the world in AI innovation. Yet focusing solely on the United States and China elides global AI adoption dynamics and yields an incomplete picture about how and why countries acquire certain emerging technologies. While the U.S. and China undoubtedly matter when it comes to fostering AI innovation, cultivating AI talent, generating technology exports to emerging markets, and advancing AI global standard-setting, a diverse range of countries also exert significant influence on AI acquisition and adoption trends.
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    The strategic logic of digital disinformation: Offence, defence and deterrence in information warfare
    (Taylor & Francis, 2023-01-01) Ünver, Hamid Akın; Ertan, A. S.; International Relations; Arcos, R.; Chiru, I.; Ivan, C.; ÜNVER, Hamid Akın
    Why do countries engage in disinformation campaigns even though they know that they will likely be debunked later on? We explore a core puzzle in information warfare in which countries that pursue disinformation to confuse and demobilise their adversaries usually suffer from reputational penalties after they are debunked, yet they nonetheless continue to pursue such tactics. In order to explain this dilemma, we employ a formal model and walk through anarchy, pre-emption and cost miscalculation explanations of disinformation and demonstrate that countries may rationally engage in disinformation campaigns if they have a different calculus about reputational costs, if they believe their adversaries will not be able to debunk their claims successfully, and if those adversaries will not be able to disseminate their debunked claims well enough to incur reputational costs on the initiator. Ultimately, we suggest that deterrence in information warfare is attainable if the “defender” can signal its debunking and “naming-shaming” capacity prior to the disinformation campaign and if it can mobilise the support of the international audience against the attacker. We conclude by arguing that a country’s fact-checking ecosystem and its pre-existing perception within the mainstream international digital media environment are the strongest defences against disinformation.
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    ArticlePublicationOpen Access
    Using social media to monitor conflict-related migration: A review of implications for A.I. forecasting
    (MDPI, 2022-09) Ünver, Hamid Akın; International Relations; ÜNVER, Hamid Akın
    Following the large-scale 2015–2016 migration crisis that shook Europe, deploying big data and social media harvesting methods became gradually popular in mass forced migration monitoring. These methods have focused on producing ‘real-time’ inferences and predictions on individual and social behavioral, preferential, and cognitive patterns of human mobility. Although the volume of such data has improved rapidly due to social media and remote sensing technologies, they have also produced biased, flawed, or otherwise invasive results that made migrants’ lives more difficult in transit. This review article explores the recent debate on the use of social media data to train machine learning classifiers and modify thresholds to help algorithmic systems monitor and predict violence and forced migration. Ultimately, it identifies and dissects five prevalent explanations in the literature on limitations for the use of such data for A.I. forecasting, namely ‘policy-engineering mismatch’, ‘accessibility/comprehensibility’, ‘legal/legislative legitimacy’, ‘poor data cleaning’, and ‘difficulty of troubleshooting’. From this review, the article suggests anonymization, distributed responsibility, and ‘right to reasonable inferences’ debates as potential solutions and next research steps to remedy these problems.