Artificial Intelligence Economic Crimes: Threats and Solutions
Keywords:
Artificial Intelligence, Threat, Criminal Behavior, SolutionAbstract
Research and regulation of artificial intelligence aim to balance the benefits of innovation against any potential harm and disruption. However, one of the unintended consequences of the recent surge in AI research is the potential redirection of AI technologies to facilitate criminal activities, which we refer to as AI crime. In theory, this is made possible through published experiments in automating fraud targeting social media users, as well as demonstrations of AI-based manipulations of simulated markets. Nevertheless, since AI crime is still a relatively nascent and inherently interdisciplinary field—ranging from socio-legal studies to independent and formal sciences—it is not yet possible to make definitive judgments about its future. This article provides the first systematic and interdisciplinary research analysis of foreseeable threats posed by AI crime and offers law enforcement and policymakers a combination of current challenges and potential solutions.
References
Alazab, M., & Broadhurst, R. (2016). Spam and Criminal Activity. Trends and Issues in Crime and Criminal Justice, 526. https://doi.org/10.1080/016396290968326
Andrighetto, G., Governatori, G., Noriega, P., & van der Torre, L. (2013). Normative Multi-Agent Systems (Vol. 4). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
Bendel, O. (2017). The Synthetization of Human Voices. AI & SOCIETY Online First. https://doi.org/10.1007/s00146-017-0748-x
Bilge, L., Strufe, T., Balzarotti, D., Kirda, E., & Antipolis, S. (2009). All Your Contacts Are Belong to Us : Automated Identity Theft Attacks on Social Networks. Www 2009, 551-560. https://doi.org/10.1145/1526709.1526784
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., & et al. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation.
Chen, Y., Chen, P., Song, R., & Korba, L. (2004). Online Gaming Crime and Security Issues - Cases and Countermeasures from Taiwan. Proceedings of the 2nd Annual Conference on Privacy, Security and Trust.
Cliff, D., & Northrop, L. (2012). The Global Financial Markets: An Ultra-Large-Scale Systems Perspective. Monterey Workshop 2012: Large-Scale Complex IT Systems. Development, Operation and Management, 29-70. https://doi.org/10.1007/978-3-642-34059-8_2
Danaher, J. (2017). Robotic Rape and Robotic Child Sexual Abuse: Should They Be Criminalised? Criminal Law and Philosophy, 11(1), 71-95. https://doi.org/10.1007/s11572-014-9362-x
Darling, K. (2016). 'Who's Johnny' Anthropomorphic Framing in Human-Robot Interaction, Integration, and Policy. https://doi.org/10.1093/oso/9780190652951.003.0012
Ezrachi, A., & Stuck, M. E. (2016). Two Artificial Neural Networks Meet in an Online Hub and Change the Future (of Competition, Market Dynamics and Society). https://doi.org/10.2139/ssrn.2949434
Ferrara, E. (2015). Manipulation and Abuse on Social Media. https://doi.org/10.1145/2749279.2749283
Floridi, L., & Taddeo, M. (2016). What Is Data Ethics? Philosophical transactions of the royal society a: mathematical, physical and engineering sciences, 374(2083). https://doi.org/10.1098/rsta.2016.0360
Gogarty, B., & Hagger, M. (2008). The Laws of Man over Vehicles Unmanned : The Legal Response to Robotic Revolution on Sea , Land and Air. Journal of Law, Information and Science, 19, 73-145. https://doi.org/10.1525/sp.2007.54.1.23
Graeff, E. C. (2014). What We Should Do Before the Social Bots Take Over: Online Privacy Protection and the Political Economy of Our Near Future.
Hildebrandt, M. (2008). Ambient Intelligence, Criminal Liability and Democracy. Criminal Law and Philosophy, 2(2), 163-180. https://doi.org/10.1007/s11572-007-9042-1
Kerr, I. R. (2004). Bots, Babes and the Californication of Commerce. University of Ottawa Law & Technology Journal, 287-324.
Kolosnjaji, B., Demontis, A., Biggio, B., Maiorca, D., Giacinto, G., Eckert, C., & Roli, F. (2018). Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables ARXIV - 1803.04173.
Lessig, L. (1999). Code and Other Laws of Cyberspace. New York: Basic Books.
Mackey, T. K., Kalyanam, J., Katsuki, T., & Lanckriet, G. (2017). Machine Learning to Detect Prescription Opioid Abuse Promotion and Access via Twitter. American Journal of Public Health, 107(12), e1-6. https://doi.org/10.2105/AJPH.2017.303994
Marrero, T. (2016). Record Pacific Cocaine Haul Brings Hundreds of Cases to Tampa Court. Tampa Bay Times.
Martínez-Miranda, E., McBurney, P., & Howard, M. J. (2016). Learning Unfair Trading: A Market Manipulation Analysis From the Reinforcement Learning Perspective. Proceedings of the 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016, 103-109. https://doi.org/10.1109/EAIS.2016.7502499
McAllister, A. (2017). Stranger than Science Fiction: The Rise of A.I. Interrogation in the Dawn of Autonomous Robots and the Need for an Additional Protocol to the U.N. Convention Against Torture. Minnesota Law Review, 101, 2527-2573. https://doi.org/10.3366/ajicl.2011.0005
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. August.
Pagallo, U. (2011). Killers, Fridges, and Slaves: A Legal Journey in Robotics. AI and Society, 26(4), 347-354. https://doi.org/10.1007/s00146-010-0316-0
Sætenes, G. M. (2017). Manipulation and Deception with Social Bots : Strategies and Indicators for Minimizing Impact.
Seymour, J., & Tully, P. (2016). Weaponizing Data Science for Social Engineering: Automated E2E Spear Phishing on Twitter.
Twitter - Impersonation Policy. (2018). Twitter.
Veletsianos, G., Scharber, C., & Doering, A. (2008). When Sex, Drugs, and Violence Enter the Classroom: Conversations between Adolescents and a Female Pedagogical Agent. Interacting with Computers, 20(3), 292-301. https://doi.org/10.1016/j.intcom.2008.02.007
Wellman, M. P., & Rajan, U. (2017). Ethical Issues for Autonomous Trading Agents. Minds and Machines, 27(4), 609-624. https://doi.org/10.1007/s11023-017-9419-4
Williams, R. (2017). Lords Select Committee, Artificial Intelligence Committee, Written Evidence (AIC0206).
Zhou, W., & Kapoor, G. (2011). Detecting Evolutionary Financial Statement Fraud. Decision Support Systems, 50(3), 570-575. https://doi.org/10.1016/j.dss.2010.08.007
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