AI in crime prevention policies
Big data analytics is gaining ground in policy-making because of the use of algorithms as predictive tools for risk analysis or crime prevention. This gives rise to the concept of predictive policing, which involves predicting the time and place of future criminal activity through data analytics. This may also include identifying “individuals who are at a risk of committing a crime in the near future” or creating “profiles that accurately match likely offenders based on data from past crimes”. One of the major functional areas that have stimulated the civil development of AI is voice and speech recognition, as a subfield of AI that blends acoustics, natural language processing, and linguistics, in order to recognize the speaker and/or the meaning associated with a spoken input.
When it comes to human rights and democracy, the AI implications pose pressing challenges. In that case, AI is not confined to logical or technological questions, but it shall interact with the society and here ethical dilemmas arise. For combating crime through the application of AI, research is yet in an experimental phase, though for crime prevention, predictive analytics systems are implemented in the market, used to make predictions of the likelihood of an event to take place. There are though a few limitations, beginning from data collection, processing, and storage, the type of data used to train the algorithms, how the algorithm is initially constructed, leading to the significant risk of error in the prediction. Predictive systems, while capable of being beneficial with the correct data, present a risk should they be provided with poor data or attacked in a manner that compromises their decision-making.
MARVEL’s success relies on data capture and processing at the application of AI technology in the cloud system and the matter of ethics and regulation.
How does it work?
In order to achieve performance improvement for AI, increased data access is required. AI technology develops a hybrid cloud applications environment, and accessibility as well as seamless movement enhances crucial connectivity toward supporting order processing and facilitating effective management of organization activities. Streamlining cloud applications with AI technology can contribute to smarter decision-making in an organizational environment. Therefore, when an organization connects data to cloud applications and AI technology, the organization can identify patterns and trends in various data sets, including technologies that make cities smarter or assist organizations to identify and prevent crime. A suggestions generator that leads to solutions is necessary to involve the “customers”, hence establishing a stronger relationship, since understanding the greater cause may lead to a more successful result. Therefore, artificial intelligence in the cloud may lead to better (and smarter) decisions, improve the performance in data processing and propose intelligent strategies.
MARVEL goes beyond the more traditional edge-only and cloud-only architecture designs via the edge-fog-cloud computing continuum paradigm for data collection, data and resource management, distribution, training, inference, visualization, and decision-making. In addition, MARVEL will improve upon the standard federated learning paradigm. Furthermore, MARVEL architecture introduces a novel module for deployment optimization and management, responsible for continuous maintenance and improvement of data capture and ML/DL deployment at all levels of MARVEL’s E2F2C architecture
Apart from the innovative MARVEL technology framework, attention is given to reduce the ethical dilemmas created by AI. Ethics are a big chapter during the implementation of MARVEL in order to balance innovation and regulation and that is the reason why an Ethics and Advisory Board (EAB) is established. The EAB will safeguard for the potential challenges that may arise in terms of data security including issues on confidentiality, integrity, data access, authentication, and authorization as well as data breach. In the case where AI uses data in order to extract information and proceed with decisions, people shall be able to control the information they are providing and thus have control over the data. This type of privacy protection shall be legally regulated in order to bring penalties in case of non-compliance. For example, a user may having open an application for navigation, but through its operation, other, additional data may be collected from the surrounding environment. This could be from a private conversation to a sound that may reveal criminal activity. The user shall be aware of this operation, and at the same time to be able to turn on/off this function at any time. At the same time, legal provisions shall be in place in order to safeguard the type of information received and stored.
Data collection and data storage
In order to construct AI patterns and train the algorithms, it is necessary to collect a series of data and store them properly. Regulations such as the GDPR have been established in order to avoid data misuse and provide legal assurance for data protection. Data protection is a variable that is challenged by the constant evolution of technology, artificial intelligence, and big data. Legislation should protect but also be flexible to change in order to promote innovation without disadvantaging rights. Information, proper use, and storage can improve the outcome and the way a situation is managed.
 Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., & Hollywood, J. S. (2013). Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. Santa Monica, CA: RAND
 Russell, S. J., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach (2nd ed.). Upper Saddle River, NJ: Prentice Hal
 Roderic Broadhurst, Paige Brown, Donald Maxim, Harshit Trivedi, and Joy Wang, (2019). Artificial Intelligence and Crime, Research Paper, Korean Institute of Criminology and Australian National University Cybercrime Observatory, College of Asia and the Pacific, Canberra, June 2019.
 Ibid. Roderic Broadhurst et. al.
 Cascio, Wayne & Montealegre, Ramiro. (2016). How Technology Is Changing Work and Organizations. Annual Review of Organizational Psychology and Organizational Behavior. 3. 349-375. 10.1146/annurev-orgpsych-041015-062352.
 See *Surya, Lakshmisri. (2018). STREAMLINING CLOUD APPLICATION WITH AI TECHNOLOGY. SSRN Electronic Journal. 5. 23. *Bharathi, N. & Neelamegam, Pasupathy. (2013). A Reconfigurable Framework for Cloud Computing Architecture. Journal of Artificial Intelligence. 6. 117-120. 10.3923/jai.2013.117.120.
- Project Coordinator: Dr. Sotiris Ioannidis
- Institution: Foundation for Research and Technology Hellas (FORTH)
- E-mail: firstname.lastname@example.org
- Start: 01.01.2021
- Duration: 36 months
- Participating Organisations: 17
- Number of countries: 12
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.