Erőszakos radikalizmus kockázatbecslése – elméleti háttér és kutatási eredmények

Abstract

Összefoglalás. A tanulmány a terrorveszély felismerésének és kezelésének elméleti és technikai összefoglalását nyújtja. Kiemelten foglalkozik a kockázatbecslés technikájával, és bemutat egy új, mesterséges intelligencián alapuló eljárást, amelynek segítségével 90%-os sikereséllyel lehet azonosítani azokat, akik terrortámadásokat hajthatnak végre. E mellett, az eljárás által szolgáltatott adatok felhasználásával sikerült leírni a radikálisok két típusát, amelyeket megközelítőleg az „alárendelt/erőszakos” és a „vezető/nem-erőszakos” szavakkal jellemezhetnénk. A két csoport között jelentős különbségek voltak a családi háttér, iskolázottság, radikalizálódási folyamat, állampolgársági múlt, bűnözői előélet, és szerepvállalási jellegzetességek tekintetében. Summary. Although Hungary is in a privileged position regarding the threat of terrorism, the history of other countries suggests that similar good positions can be temporary. The threat of terrorism can be investigated by several scientific approaches. After reviewing these, we analyze the theoretical and technical background of risk assessment, and present the results of our recently concluded research. In this we examined the US database PIRUS, which contained 112 types of personal data of 2,148 radicals. About half of them did carry out terrorist attacks the other half did not. Based on the individual characteristics of the radicals, the XGBoost machine learning algorithm correctly identified the perpetrators of the terrorist attacks with a probability of 87%. By using the data provided by the software, it was also possible to describe two types of radicals, which could be roughly characterized by the words “subordinate/violent” and “leader/non-violent”. The former usually had a criminal but not a radical background. They converted late in life (if their radicalization was of a religious nature) and adopted radical ideas as adults (if their radicalization was nonreligious in nature). They played a subordinate role in terrorist groups, required training and were largely influenced by social media. They also belonged to low social classes and had many personal problems. In contrast, non-violent extremists were characterized by a family tradition of radicalism, mostly had no criminal past, belonged to higher social strata, and played leading roles in terrorist organizations. Instead of committing attacks, they engaged in illegal activity by supporting terrorist organizations. The two main types probably consist of subtypes. Compared to violent extremists who were radicalized in prison, for example, those who were not radicalized in prison were mostly foreigners, were often unemployed despite their higher education, and compared to those radicalized in prison, they committed lesser crimes before radicalization. Similar subgroups occurred in both main groups, but their detailed characterization requires further research. Our findings suggest that artificial intelligence can become a good tool for the risk assessment of radicals concerning their proneness to perform terrorist attacks. Moreover, the risk assessment tool employed here may be useful in typifying radicals, and studying their radicalization routes.