Query: artificial intelligence crime

title: Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions
abstract: Artificial intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re orientation of AI technologies to facilitate criminal acts, term in this article AI Crime (AIC) AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently interdisciplinary area spanning socio legal studies to formal science there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space.

title: Artificial intelligence: accelerator or panacea for financial crime?
abstract: PurposeThis purpose of this viewpoint is to address the intended good and unintended bad impacts of artificial intelligence (AI) applications in financial crime. paper relied primarily on secondary data resources, business cases and relevant laws and regulations, and it used a legal economics perspective.FindingsCurrent AI systems could function as antidotes or accelerator of financial crime, in particular cybercrime. Research suggests criminal law could be applied via three approaches to curb these cybercrimes. However, others considered this to be an inappropriate mechanism to hold AI agents accountable, as present AI systems were not deemed capable of making ethically informed choices. Instead, administrative sanctions would be considered more appropriate for now. While keeping vigilance against AI malicious acts, regulatory authorities in the USA and the UK have opted largely for the innovation friendly, market oriented, permissionless approach over the state interventionist stance so as to maintain their global competitive edge in this domain. paper reinforced the growing arguments that AI applications should be deployed more as panacea for financial crimes rather than being abused as crime accelerators. There equally though is the need for both public and private sectors to be mindful of the unintended negative, harmful consequences to society, especially those connected to cybercrime. This implied the further need to beef up attention and resources to help mitigate these risks.

title: Where Are We? Using Scopus to Map the Literature at the Intersection Between Artificial Intelligence and Crime
abstract: Research on Artificial Intelligence (AI) applications has spread over many scientific disciplines. Scientists have tested the power of intelligent algorithms developed to predict (or learn from) natural, physical and social phenomena. This also applies to crime related research problems. Nonetheless, studies that map the current state of the art at the intersection between AI and crime are lacking. What are the current research trends in terms of topics in this area? What is the structure of scientific collaboration when considering works investigating criminal issues using machine learning, deep learning, and AI in general? What are the most active countries in this specific scientific sphere? Using data retrieved from the Scopus database, this work quantitatively analyzes 692 published works at the intersection between AI and crime employing network science to respond to these questions. Results show that researchers are mainly focusing on cyber related criminal topics and that relevant themes such as algorithmic discrimination, fairness, and ethics are considerably overlooked. Furthermore, data highlight the extremely disconnected structure of co authorship networks. Such disconnectedness may represent a substantial obstacle to a more solid community of scientists interested in these topics. Additionally, the graph of scientific collaboration indicates that countries that are more prone to engage in international partnerships are generally less central in the network. This means that scholars working in highly productive countries (e.g. the United States, China) tend to mostly collaborate domestically. Finally, current issues and future developments within this scientific area are also discussed.

title: Artificial Intelligence and Crime
abstract: This review of the rapidly growing literature on the impact of artificial intelligence (AI) on crime and crime prevention outlines some of the likely limits and potential of AI applications as a crime enabler or crime fighter. In short, our focus is on the criminogenic potential as well as preventative role of AI. Investment and interest in developing machine learning (ML) technologies that underpin AI capabilities across industry, civil and military applications have grown exponentially in the past decade. This investment in AI research and development has boosted innovation and productivity as well as intensifying competition between states in the race to gain technological advantage. The scale of data acquisition and aggregation essential for the 'training' of effective AI via ML processes also poses broader associated risks to privacy and fairness as such technology shifts from a niche to general purpose technology. The potential weaponization of AI applications such as computational marketing, 'deep' fake images or news and enhanced surveillance, however, are pressing challenges to democracies and human rights. Poorly implemented ethical, accountability and regulatory measures will also provide opportunities for criminal activity as well as the potential of accidents, unreliability and other unintended consequences. AI is generally described as weak, medium or strong and it is widely understood that the AI currently in use can be classified as weak. This is because the underlying ML iterative processes are based on domain specific or limited 'training' particular to the problem or activity addressed and thus offer only narrow or highly focused AI applications. Five major functional areas have stimulated the civil development of AI and these are: Voice and Speech Recognition Computer Vision Extended Reality (XR) Game Playing Natural Language Processing (NLP) We explore some of the concerns about AI as currently realised and argues that automated decision making technology should not be relied upon as a sole decision making tool. Scepticism is currently necessary when evaluating an AI's performance. The United States White House's Office of Science and Technology Policy has warned of the potential threat that AI poses to privacy, civil rights, and individual freedoms through the "Potential of encoding discrimination in automated decisions" (Executive Office of the US President, 2016, p. 45) The various ways in which AI can be used as a defensive or an offensive tool in respect to cybercrime are described and evaluate some of the opportunities and risks that arise when AI is employed either to defend and detect attacks against computer systems or to attack computer systems. A summary of the legal and regulatory dimensions of AI and discusses the extent to which existing laws are sufficiently technologically neutral or 'future proof' to incorporate the use and potential 'misconduct' or misuse of AI in decision making. AI applications will also be used to enhance many existing surveillance systems incorporating and aggregating data from different sources including fusing individual biometric data with expenditure and activity transaction records to create 'whole of life' behavioural classifiers. PR China's newly piloted social responsibility system is such a system that 'scores' an individual citizen's conduct (e.g. by rating the 'social worthiness' of consumption and activities) so as to prioritise access to state controlled activities or services (e.g. state offices/jobs, travel, educational or medical services) The ubiquitous melding of data to classify conduct and rate individuals driven by AI algorithms may emerge as the key method of the new surveillance state or corporation. In this sense, AI may also evolve as a self preserving autonomous system. These new forms of total surveillance will also be a key target for manipulation by criminal actors and others.

title: Comparative Study on Artificial Intelligence Techniques in Crime Forecasting
abstract: An application of efficient crime analysis is beneficial and helpful to understand the behavior of trend and pattern of crimes. Crime forecasting is an area of research that assists authorities in enforcing early crime prevention measures. Statistical technique has been widely applied in the past to develop crime forecasting models. However, it has been observed that researchers have begun to shift their research interests from statistical model to artificial intelligence model in crime forecasting. Thus, this study is conducted to observe the capabilities of artificial intelligence technique in improving crime forecasting. The main objective of this study is to conduct a comparative analysis on forecasting performance capabilities of four artificial intelligence techniques, namely, artificial neural network (ANN) support vector regression (SVR) random forest (RF) and gradient tree boosting (GTB) in forecasting crime rate. Forecasting capability of each technique was assessed in terms of measurement of errors. From the result obtained, GTB showed the highest performance capability where it scored the lowest measurement of errors compared to SVR, RF, and ANN.

title: Artificial Intelligence for Solving Crime
abstract: Artificial intelligence, which is already helping astronauts on the ISS, is also providing a promising approach for solving crimes.

title: On the Possibility of Doctrinal Perception of Artificial Intelligence as the Subject of Crime in the System of Common Law: Using the Example of the U.S. Criminal Legislation
abstract: The authors examine the possibility of holding artificial intelligence (AI) criminally liable under the current U.S. criminal legislation and study the opinions of Western lawyers who believe that this possibility for a machine controlled by AI may become reality in the near future. They analyze the requirements for criminal liability as determined by American legislators: a willful unlawful act or omission of an act (actus reus) criminal intent (mens rea) i.e. the person knowingly commits a criminal act or is negligent, as well as three basic models of AI's criminal liability. In the first model, a crime is committed through the actions of another person, i.e. the cases when the subject of crime does not have sufficient cognitive abilities to understand the criminal intent and, moreover, to be guided by it. This category of persons includes minors, persons with limited legal capacity and modern cybernetic systems, who cannot be viewed as capable of cognition that equals human cognition. The latter are considered to be innocent of a criminal act because their actions are controlled by an algorithm or a person who has indirect program control. In the second model, a crime is committed by a being who is objectively guilty of it. A segment of the program code in intellectual systems allows for some illegal act by default, for example, includes a command to unconditionally destroy all objects that the system recognizes as dangerous for the purpose that such AI is working to fulfill. According to this model, the person who gives the unlawful command should be held liable. If such a <collaborator> is not hidden, criminal liability should be imposed on the person who gives an unlawful command to the system, not on the performer, because the algorithmic system that determines the actions of the performer is itself unlawful. Thus, criminal liability in this case should be imposed on the persons who write or use the program, on the condition that they were aware of the unlawfulness of orders that guide the actions of the performer. Such crimes include acts that are criminal but cannot be prevented by the performer the AI system. In the third model, AI is directly liable for the acts that contain both a willful action and the unlawful intent of the machine. Such liability is possible if AI is recognized as a subject of criminal law, and also if it independently works out an algorithm to commit an act leading to publically dangerous consequences, or if such consequences are the result of the system's omission to act according to the initial algorithm, i.e. if its actions are willful and guilty.

title: AICAMS: artificial intelligence crime analysis and management system
abstract: Expert systems and artificial intelligence technologies have a significant role to play in the development of tools to support police operations. Systems have been successfully demonstrated in the areas of breaking and entering, criminal profiling, tracking serial criminals and others. In this paper, we describe the development of a system that will support police investigations in several crime areas. The methodology has been adapted from that used in several North American breaking and entering projects, to meet the requirements of the Hong Kong police. The system is initially rule based, with provisions for investigating machine learning and neural network techniques to improve performance and makes extensive use of existing data sources. The development of the initial demonstration system is described and the methodology is discussed.

title: ARTIFICIAL INTELLIGENCE AS VICTIM OF CRIME

title: ARTIFICIAL INTELLIGENCE IN CRIME PREVENTION AND CONTROL