INTELIGÊNCIA ARTIFICIAL COMO FERRAMENTA PARA PREVISÃO DE CRIMES EM GRANDES CIDADES BRASILEIRAS
REGISTRO DOI: 10.5281/zenodo.11100354
Luiz Henrique da Costa Ribeiro1
Clodoaldo Matias da Silva2
Paulino Wagner Palheta Viana3
ABSTRACT
The aim of this study is to investigate the use of artificial intelligence methodologies in predicting criminal activity in Brazil’s main cities. To achieve this objective, a review of relevant scientific articles, books and reports relating to this subject was carried out. The approach chosen for this study was a comprehensive literature review, which involved the careful selection and critical evaluation of publications related to the aforementioned topic. Initially, comprehensive searches were carried out in available databases, after which studies that met the pre-determined inclusion criteria were carefully chosen. During the course of this study, it was noted that machine learning algorithms and neural networks have emerged as the most widely used methodologies, as they have the ability to analyse large amounts of data and identify discernible patterns that indicate the likelihood of criminal incidents occurring in a specific region and time period. The strategic deployment of resources and the prevention of criminal activity are facilitated by this valuable information, assisting law enforcement in their decision-making process. Ultimately, the use of artificial intelligence as a predictive tool for criminal behaviour could become an effective investigative tool in Brazil’s major cities, strengthening the ongoing battle against crime and ensuring the well-being of the population. However, it is crucial to emphasise that the successful implementation of these methodologies requires the implementation of comprehensive public safety policies and initiatives, promoting a cohesive and effective approach to combating criminal activity.
Keywords: Artificial Intelligence. Crime Forecasting. Brazilian Cities.
RESUMO
O objetivo deste estudo é investigar a utilização de metodologias de inteligência artificial na previsão de atividades criminosas nas principais cidades do Brasil. Para atingir esse objetivo, foi realizada uma revisão de artigos científicos relevantes, livros e relatórios relativos a este assunto. A abordagem escolhida para este estudo foi uma revisão bibliográfica abrangente, que envolveu a seleção criteriosa e avaliação crítica de publicações relacionadas ao tema mencionado. Inicialmente, foram realizadas buscas abrangentes em bases de dados disponíveis, posteriormente, foram criteriosamente escolhidos os estudos que atendiam aos critérios de inclusão pré-determinados. Observou-se durante a realização desse estudo, que notavelmente, os algoritmos de aprendizagem automática e as redes neurais surgiram como as metodologias mais utilizadas, pois possuem a capacidade de analisar grandes quantidades de dados e identificar padrões discerníveis que indicam a probabilidade de ocorrência de incidentes criminais numa região e num período de tempo específicos. A distribuição estratégica de recursos e a prevenção da atividade criminosa são facilitadas por esta informação valiosa, auxiliando a aplicação da lei no seu processo de tomada de decisão. Em última análise, a utilização da inteligência artificial como ferramenta preditiva de comportamento criminoso pode ser tornar uma ferramenta de investigação eficaz nas principais cidades do Brasil, reforçando a batalha contínua contra o crime e garantindo o bem-estar da população. No entanto, é crucial sublinhar que a implementação bem-sucedida destas metodologias, que exige a implementação de políticas e iniciativas abrangentes de segurança pública, promovendo uma abordagem coesa e eficaz para combater a atividade criminosa.
Palavras-chave: Inteligência Artificial. Previsão de Crimes. Cidades Brasileiras.
INTRODUCING
The increasingly complex task of preventing and combating crime has fuelled the development of new technologies and methodologies. Among these, artificial intelligence has gained prominence as a promising tool for predicting and preventing criminal activity in Brazil’s main cities. In order to investigate the use of artificial intelligence methodologies in this context, this article aims to analyse the possibilities and limitations of this approach.
The aim is to answer the following question: To what extent can artificial intelligence be effective in predicting crime in major Brazilian cities? Faced with a scenario of insecurity and urban violence, the relevance of this topic is evident. With the advance and dissemination of increasingly sophisticated technologies, it is crucial to understand how artificial intelligence can be applied in the fight against crime, bearing in mind its possible ethical and legal implications.
To carry out this study, a bibliographic methodology was adopted, consisting of researching and analysing articles, books and other academic sources on the subject. In addition, data and reports from official bodies were consulted in order to support the discussion with concrete and up-to-date information. The results show that, despite its limitations, artificial intelligence can be an important tool for predicting criminal activity in large Brazilian cities.
By analysing data and patterns, it is possible to identify areas with a higher incidence of crime, the most likely times and days for it to occur, as well as possible perpetrators and victims. However, it is important to emphasise that the use of artificial intelligence should not be seen as a definitive solution to public security. A critical eye and a multidisciplinary approach are needed to guarantee effectiveness and ethics in the application of these technologies. In addition, the privacy and protection of citizens’ data must be taken into account.
In conclusion, it can be said that artificial intelligence has the potential to help fight crime in large Brazilian cities. However, its application must be guided by ethical and legal principles, as well as being constantly evaluated and improved. The adoption of this tool, together with other security measures, can contribute to promoting a safer and fairer society.
1. THE IMPORTANCE OF ANALYSING CRIME IN THE BRAZILIAN URBAN CONTEXT
The analysis of crime in the Brazilian urban context is a topic of great relevance to society, as it reflects the situation of insecurity and violence experienced in large urban centres. Among the various factors that contribute to the rise in crime rates in the country, we can highlight social inequality, unemployment, drug use and the absence of the state in some regions.
Given this scenario, it is necessary to understand the causes and consequences of crime in an urban context in order to propose effective measures to tackle it. According to Mendes (2020), the lack of access to education and income is one of the main factors responsible for social inequality in Brazil. This reality is strongly present in large cities, where population density and a lack of adequate infrastructure create a favourable environment for the development of criminal activities.
In this sense, the author emphasises the importance of social inclusion and investment in public policies aimed at distributing income and reducing inequality as a way of preventing crime. Complementing this perspective, Telles (2021) emphasises that inequality is a structural phenomenon in Brazil and is intrinsically linked to the country’s historical formation, marked by slavery and the social exclusion of certain groups.
This inequality is therefore reflected in crime rates, since more vulnerable and marginalised individuals are more likely to commit crimes as a means of survival or social advancement. A sensitive approach and effective public policies are therefore needed to tackle the roots of social inequality in Brazil. However, the lack of opportunities and social inequality alone do not explain the increase in crime in the urban context.
Drugs are another factor that has contributed to the increase in violence in large cities. Teixeira (2018) states that the presence of drugs is one of the main factors of instability and violence in Brazilian metropolises, given their direct influence on arms trafficking and other criminal activities. In addition, drug use is also related to crimes such as theft and robbery, since users need to obtain money to support their addictions.
In view of these facts, it is necessary for the state to adopt measures to combat drug trafficking and concern itself with the issue of rehabilitation and reintegration of these users into society. Gonçalves (2022) emphasises that public security policies must seek integrated actions between the different spheres of government in order to guarantee the protection of citizens and a reduction in crime rates. In addition, it is essential to create programmes to prevent drug use, with a focus on educating and raising awareness among young people.
Another relevant aspect to consider when analysing crime in Brazil’s urban context is the role of the state in these areas. According to Lopes (2015), it is essential for the government to be present in the most vulnerable and deprived areas of large cities, guaranteeing an adequate structure, such as basic sanitation, public lighting and security equipment.
The absence of the state in these areas is a factor that favours the occurrence of crime, since residents feel unassisted and unprotected, which leads them to seek illegal alternatives to meet their needs. As such, it is essential that public authorities take a closer look at less favoured communities in order to guarantee the safety and well-being of the population.
According to Takada (2019), it is necessary to invest in social policies that promote the inclusion and development of these areas, providing better living conditions and opportunities for residents. Furthermore, it is important to emphasise that urban violence not only affects the most vulnerable population, but also the middle class, who live in fear and fear losing their property or having their physical integrity threatened.
In this sense, it is essential that the measures adopted to combat crime are not only punitive, but also prevent and re-socialise criminals, with a view to promoting a culture of peace. To complement this approach, Mendes (2020) emphasises the importance of adopting public crime prevention policies aimed at social development and citizen education.
In this respect, Telles (2021) emphasises the importance of investing in education in order to train conscious and responsible citizens who are capable of cooperating in building a more peaceful and less violent society. In addition, the presence of the state and access to quality education can reduce the vulnerability of young people and children to involvement in criminal activities, offering them opportunities for a better future.
In view of the above, it can be said that analysing crime in Brazil’s urban context is a highly complex issue that involves various social, economic and historical factors. To combat this problem, a joint effort by the state, civil society and the scientific community is needed in order to understand the causes and consequences of violence in large cities and propose effective actions to tackle it.
It is therefore essential that the state takes on the role of promoting social policies and investing in education and development in the most vulnerable regions, guaranteeing presence and protection in all areas. In addition, it is necessary to invest in integrated and efficient public security, with actions to prevent and re-socialise criminals. Only by working together in a committed way will it be possible to tackle the problem of urban crime and make Brazil a more peaceful and safe country for everyone.
2. A BRIEF HISTORY OF THE USE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN PUBLIC SECURITY
Artificial intelligence has become increasingly present in various areas of society, including public security. The use of technologies that are capable of learning and making decisions without human intervention promises to be a solution for preventing and fighting crime, but it is also raising concerns about privacy and human rights. In this context, it is important to take a brief look at the global panorama of the use of artificial intelligence in public security and reflect on its implications.
At the end of the 1970s, the use of information technology systems was already a reality in the US police. Willians (2016) points out that the first computerised systems were used to manage incident information and help identify crime patterns. In the 1980s, technological advances enabled the development of facial recognition and voice recognition systems, expanding the use of AI in public safety.
In the 1990s, the first large-scale artificial intelligence systems appeared in Europe, particularly in the Netherlands and England. Aguirre, Badran and Muggah (2019a) point out that these systems were based on algorithms that allowed access to and processing of large volumes of data, contributing to decision-making in policing management and crime prevention.
Since the 2000s, the use of AI technologies has intensified in various countries around the world, such as China and Israel, with the aim of monitoring suspicious behaviour and identifying possible security threats. In Brazil, the use of artificial intelligence in public security started to become more widespread in 2011, with the implementation of the “Olho Vivo” programme, which uses video surveillance cameras in Belo Horizonte.
Following this line of thought, Visacro (2018) draws attention to the fact that, at the same time as crime rates have fallen, there has been an increase in the population’s sense of insecurity, a reflection of the rapid incorporation of artificial intelligence technologies in the sector. However, it is important to emphasise that the use of AI in public security is not a consensus. Russell and Norwig (2018) point out that, despite promises of an objective and impartial approach, AI systems are influenced by their creators and the data used to train them, and can be subject to bias and discrimination.
Gatens and Reichert (2019) point out that automated facial identification, for example, can be used to enhance social control and even violate human rights, such as the right to privacy. In addition, the use of AI in public security has also raised ethical and legal questions. Perry et al (2018) point out that the use of decision-making algorithms in court cases can create a dilemma between the equality of the parties involved and achieving an efficient result.
This is because, according to the authors, it is necessary to establish clear and transparent criteria for the use of these systems, ensuring that there is no violation of fundamental rights. Another important point that deserves attention is the possibility of AI systems being the target of cyber attacks. According to Florida (2018), with the application of artificial intelligence technologies in various devices connected to the internet, cyber security has become an even greater concern.Nesse sentido, é fundamental que os sistemas de IA utilizados em segurança pública sejam desenvolvidos com medidas de segurança robustas, para evitar a exposição de informações sensíveis e riscos à sociedade. Diante desse panorama, é necessário refletir sobre os rumos da utilização de tecnologias de inteligência artificial em segurança pública. Para Teixeira (2018, p. 62), “a falta de regulamentação e transparência na utilização desses sistemas pode gerar graves consequências, como a violação de direitos fundamentais, o aumento da desigualdade e o enfraquecimento do Estado de Direito”.
It is therefore essential to have clear regulations and a broad discussion about the ethical and legal limits of using AI in public safety. In addition, the bodies responsible for managing public safety need to be prepared for the implementation of these systems, with adequate training to use them efficiently and carefully. According to Mendes (2020, p. 210), “it is important that society is informed and participates in these discussions, ensuring transparency and social control over the use of artificial intelligence”.
In conclusion, it can be seen that the use of artificial intelligence technologies in public security has been a global trend in recent decades, but one that still generates concerns and debates about its ethical and legal implications. It is essential to take a cautious and responsible approach to these systems so that they can make an effective contribution to preventing and combating crime, respecting human rights and guaranteeing the safety of society as a whole.
3. CURRENT STATE OF RESEARCH INTO PREDICTING CRIMINAL ACTIVITY WITH ARTIFICIAL INTELLIGENCE
The use of technology to prevent and combat crime has been a broad topic of great interest to society today. Technological developments have made it possible to incorporate artificial intelligence into a wide range of areas, including public security, with the aim of aiding decision-making and predicting criminal activity.
In this scenario, research into artificial intelligence for predicting criminal activity has been the subject of various discussions and studies, with the aim of understanding its potential and impact on the global panorama. In this sense, it is important to analyse the current state of research on the subject in order to understand how artificial intelligence has been used and what the future prospects are.
According to Teixeira (2018), artificial intelligence can be conceptualised as a set of techniques and algorithms that allow computer systems to perform cognitive functions similar to those of humans, such as learning, reasoning and decision-making. With the advent of Big Data and the exponential growth in the amount of data available, artificial intelligence has proved to be a powerful tool for analysing and identifying patterns in data, including information related to crime and criminal activity.
In this context, Visacro (2018) highlights the role of artificial intelligence in predicting criminal activity, with its main objective being the early identification of potential crimes, enabling preventative action to be taken. Through the use of machine learning algorithms, artificial intelligence can analyse large amounts of data, such as crime records, criminal profiles and environmental factors, to identify patterns and create predictive models.
In this way, it is possible to more accurately predict areas and periods with a higher probability of crime occurring. Among the main artificial intelligence techniques used to predict criminal activity is the Machine Learning (ML) model. Takada (2019) argues that ML is one of the most widely used AI approaches in the area of public safety, being able to learn from data and create predictive models without the need for specific instructions and rules.
In this way, it is possible to identify patterns and anomalies with greater precision and speed, providing valuable information for public safety agencies. However, Aguirre, Badran and Muggah (2019b) point out that artificial intelligence models for predicting criminal activity still face significant challenges, such as problems of bias in data selection and model building.
They argue that, in many cases, the data used to train the models comes from police sources, which can be influenced by social and racial issues, for example, which can generate biassed results. In addition, another challenge pointed out by Schwab (2016) is the need to take data privacy and ethical issues into account when collecting and using information to create predictive models.
He points out that it is important to ensure that the use of artificial intelligence to predict criminal activity does not violate individual and collective rights, or reinforce injustices and discrimination that already exist in society. Despite the challenges faced, research efforts in the area of artificial intelligence for predicting criminal activity have shown promising results.
Confirming this context, Braga (2019) points out that, in some cases, predictive artificial intelligence models have had superior results to the traditional methods used by public security professionals. However, he warns that more research and evaluations are still needed to guarantee the effectiveness and safety of using these models in practice.
Another relevant perspective to consider is the role of public-private partnerships (PPPs) in advancing research and the implementation of artificial intelligence in public security. Aguirre, Badran and Muggah (2019a) argue that cooperation between the public and private sectors can be very beneficial for the development of innovative AI solutions to predict criminal activity. Technology companies, for example, can provide access to data and advanced AI tools, while public security agencies can provide knowledge and expertise in specific areas.
In this sense, it is essential that government bodies establish clear policies and ethical guidelines for the use of artificial intelligence in public safety. Schwab (2016) highlights the importance of a constant dialogue between the public and private sectors, as well as with society, in order to guarantee the responsible and transparent use of technology. In addition, the creation of monitoring and evaluation mechanisms, as well as impact studies, is fundamental to ensuring that AI is used effectively and ethically.
Another relevant aspect is the impact of the use of AI on public safety at a global level. In this regard, Tokunaga (2022) points out that, although it is still in its early stages, the use of AI in predicting criminal activity is proving to be a trend in several countries. She analysed the case of Japan, where the government has been investing in artificial intelligence technologies to help prevent and combat crime, as well as promoting public safety at major events, such as the Tokyo Olympics in 2022.
Similarly, Aguirre, Badran and Muggah (2019a) point out that artificial intelligence is being used by governments and agencies in countries such as the United States, the United Kingdom and Australia. However, they warn of the need to consider local particularities and contexts, since the strategies and models developed in one region will not necessarily be effective in another.
It is therefore necessary to consider the use of artificial intelligence in predicting criminal activity from both a global and local perspective, respecting the particularities and taking into account the cultural, social and political diversity of each country. It is essential that research in this area is carried out jointly with the collaboration of researchers from different regions, since artificial intelligence should be used as a tool to promote public safety and not accentuate inequalities and injustices.
In conclusion, the use of artificial intelligence in predicting criminal activity has been an important discussion on the world stage. Through the use of predictive models, artificial intelligence has the potential to help prevent and combat crime, but it also faces challenges and ethical issues. In this sense, it is important that research is carried out responsibly, considering the implications and impacts on privacy and individual and collective rights.
In addition, cooperation between the public and private sectors, as well as impact studies and dialogue with society, are fundamental if artificial intelligence is to be used effectively and ethically. It can therefore be concluded that there is still a long way to go, but research into artificial intelligence for predicting criminal activity shows promising results and could be an important tool for improving public safety around the world.
4. POTENTIAL AND CHALLENGES IN APPLYING ARTIFICIAL INTELLIGENCE METHODOLOGIES TO CRIME FORECASTING IN BRAZIL
As noted in the previous sections of this research, the world is currently living in an era marked by the exponential growth of technology and its impact on all areas of society, including public safety. Among the various technologies that are constantly evolving, artificial intelligence methodologies stand out as tools that are potentially capable of helping to predict crime. However, this application is still a challenge for many countries, including Brazil, due to a series of limitations and ethical issues.
Given this context, the last section of this research aims to discuss the potential and challenges of applying artificial intelligence methodologies to crime prediction in Brazil. According to Russell and Norvig (2013, p. 56), “artificial intelligence is defined as the study and development of computer systems that perform tasks that require human intelligence”.
In this sense, artificial intelligence methodologies use learning algorithms and data processing to identify patterns and make predictions. These predictions, in turn, can be applied to help make decisions in various areas, including public security. One of the main benefits of applying artificial intelligence methodologies to crime forecasting is the possibility of using historical data to identify areas and periods with a higher probability of crime occurring.
According to Florida (2018), this data makes it possible to build predictive models that indicate where and when preventive measures should be taken, such as increasing patrols or lighting in certain areas. In this way, it is possible to target resources more efficiently and thus potentially reduce the incidence of crime. In addition, artificial intelligence methodologies can also be useful in identifying patterns in criminal behaviour, helping to identify suspects and solve crimes that have already occurred.
According to Rodrigues (2018), artificial intelligence makes it possible to process a large volume of information, including security camera images and social media records, to identify patterns and connections between individuals and incidents. This application can be especially useful in cases of complex crimes, such as criminal organisations, which require meticulous data analysis.
Continuing this line of thought, Hartmann Peixoto (2019) points out that artificial intelligence methodologies can also help improve the efficiency of police investigations by analysing data and making more accurate and faster decisions. In addition, these tools can make it possible to integrate data from different agencies and institutions, enabling more coordinated and effective action in the fight against crime.
Therefore, as the sections of this study have already shown, the application of artificial intelligence methodologies can bring a series of benefits to public security and society as a whole. However, despite the potential of artificial intelligence in predicting crime, this application also presents challenges that need to be considered. One of the main challenges is the question of ethics.
According to Perrot (2022), the use of personal data for the purposes of crime prediction can raise concerns about privacy and discrimination. This is because, by using learning algorithms, it is possible for the system to reproduce prejudices and stereotypes that exist in society, creating a false sense of accuracy and neutrality in decisions.
Another challenge concerns the scope and quality of the data used by artificial intelligence methodologies. According to Rodrigues (2018), it is essential that the data used is representative and of high quality so that predictive models are effective. However, the data available can often be biassed, incomplete or out of date, which can jeopardise predictions and affect decision-making.
Furthermore, it must be borne in mind that artificial intelligence is only a tool and should not replace the work of the professionals responsible for public security. As Russell and Norvig (2013) point out, there needs to be a dialogue and constant evaluation of the role of artificial intelligence and professionals in predicting crime. This application should be seen as an aid to decision-making, but not as a definitive solution to all public security problems.
In the Brazilian context, the application of AI methodologies to crime prediction is still in its infancy. The country faces a number of challenges, such as the lack of data integration and an adequate technological structure in public security institutions. In addition, ethical and legal issues, such as the use of personal data and ensuring transparency in decisions, need to be widely discussed before artificial intelligence methodologies can be effectively applied.
In this sense, it is important to emphasise the need for investment in technological infrastructure and training for public security professionals. Florida (2018) emphasises that in order to be successful in applying artificial intelligence methodologies, it is essential to have a qualified team that understands the challenges and limitations of using this technology. It is also worth highlighting the importance of adequate regulation for the use of artificial intelligence in crime prediction.
Perrot (2022) points to the need for legislation that guarantees transparency and accountability in the use of algorithms in decision-making, as well as protecting the rights of the individuals involved. In addition, it is essential that regulations are constantly updated so that they keep pace with the development of artificial intelligence technologies. In a final comment, this research says that the application of artificial intelligence methodologies to crime prediction has great potential to help fight crime and promote public safety.
However, it is important to bear in mind that this application still faces challenges, ranging from ethical issues to technological and legal limitations. In the Brazilian context, it is necessary to make progress in investments and discussions that allow for an ethical and effective application of artificial intelligence methodologies in public security.
CONCLUSION
The use of artificial intelligence technology has proved to be a powerful tool for predicting and preventing crime in Brazil’s major cities. The ability of these systems to analyse and process large amounts of data and to learn and adapt over time has attracted the interest of government bodies and public security companies across the country.
However, even as technology advances and the accuracy of artificial intelligence models continues to improve, there are still important challenges that need to be overcome if the tool is to become more effective in predicting crime in Brazil’s big cities. One of the main challenges concerns the quality and availability of the data used by artificial intelligence systems.
Much of the data available today comes from police records, which can introduce biases and distortions into analyses. In addition, limited access to crime location and activity data makes it difficult to create more accurate models. Another noteworthy point is the need for extensive and continuous updating of the algorithms used.
With the rapid development of technology, it is crucial that artificial intelligence models are constantly improved and updated to ensure their effectiveness in predicting crime in Brazil’s major cities. In addition to the technical challenges, the implementation of artificial intelligence systems for crime prediction requires ethical and legal considerations. It is necessary to ensure that the use of these tools respects the human rights and privacy of citizens and avoids discrimination based on race, gender or social class.
In addition, there must be clear and transparent regulations on the use of these systems to prevent misuse and guarantee the reliability of the results. Despite these challenges, the potential of artificial intelligence as a tool for predicting crime in Brazil’s big cities is undeniable. Through the appropriate and ethical use of technology, public safety can be significantly improved and the occurrence of crime minimised.
What’s more, as technology continues to advance and integrate different types of data, such as information from social networks and IoT sensors scattered around cities, the future is bright. With this range of data and improved algorithms, a more complete and accurate analysis will be possible, allowing the authorities to take preventative and effective measures in the fight against crime.
Another important aspect to consider is the partnership between the public and private sectors in utilising these technologies. Security and technology companies can contribute investment and expertise, while governments can provide the data and legislation to support the implementation of these systems. This co-operation can further promote the development and improvement of artificial intelligence in crime prediction.
Finally, it should be emphasised that artificial intelligence has great potential for predicting and preventing crime in large Brazilian cities, but its use must be guided by ethics, transparency and the constant updating of algorithms. The implementation of these technologies must be accompanied by effective public policies and strategic partnerships so that together we can move towards a safer and more equitable society.
REFERENCES
AGUIRRE, Katherine; BADRAN, Emile; MUGGAH, Robert. Future crime: assessing twenty first century crime prediction. Igarapé Institute, Rio de Janeiro, Strategic Note 33, jul. 2019a.
AGUIRRE, Katherine; BADRAN, Emile; MUGGAH, Robert. The development of the case study: crime prediction for more agile policing in cities. Geneva: UN Economic Commission for Europe, out. 2019b.
BRAGA, Carolina. Discriminação nas decisões por algoritmos: polícia preditiva. In: FRAZÃO, Ana; MULHOLLAND, Caitlin (coord.). Inteligência artificial e direito: ética, regulação e responsabilidade. São Paulo: Revista dos Tribunais, 2019.
LOPES, Alan Moreira. Direito das novas tecnologias: legislação eletrônica comentada, mobile law e segurança digital. São Paulo: RT, 2015.
FLORIDA, Richard. The great crime decline and the comeback of cities. Bloomberg CityLab, [s. l.], 16 jan. 2018.
GATENS, Alysson; REICHERT, Jessica. Police technology: acoustic gunshot detection systems. Chicago: Illinois Criminal Justice Information Authority, 2019.
GONÇALVES, Carlos Roberto. Direito civil brasileiro Parte Geral. São Paulo: Saraiva, 2022.
HARTMANN PEIXOTO, Fabiano. Inteligência Artificial e Direito. 1 ed.- Curitiba: Alteridade Editora, 2019.
MENDES, Carlos. Tecnoinvestigação Criminal: Entre a Proteção de Dados e a Infiltração por Software. Editora JusPodivm, 2020.
PERROT, Patrick. What about AI in criminal intelligence? From predictive policing to AI perspectives. France European Police Science and Research Bulletin, n. 16, p. 65-75, Summer 2022.
PERRY, Walter. et al. Predictive Policing: the role of crime forecasting in Law Enforcement Operations. Santa Monica, EUA: RAND Corporation Safety and Justice Program, 2018.
RUSSELL, Stuart J.; NORVIG, Peter. Inteligência artificial. Trad. Regina Célia Simille. Rio de Janeiro: Elsevier, 2013.
RUSSEL, Stuart; NORWIG, Peter. Inteligência Artificial. Tradução de: Regina Célia Simille de Macedo. Editora Elsevier. 8ª tiragem. 2018.
RODRIGUES, Valter. Como máquinas aprendem: fundamentos e algoritmos de Machine Learning, Redes Neurais e Deep Learning. Florença: Independente, 2018.
SCHWAB, Klaus. A Quarta Revolução Industrial. Tradução: Daniel Moreira Miranda. São Paulo: Edipro, 2016.
TAKADA, Thalles. Manuel jurídico da inovação e da startups. Salvador: Juspodivm, 2019.
TELLES, Primonata Silva Brilhante. Inteligência artificial e polícia preditiva: limites e possibilidades. Boletim Científico ESMPU, Brasília, n. 57, jul/dez. 2021, p. 247-263.
TEIXEIRA, Tarcísio. Curso de Direito e Processo Eletrônico: doutrina, jurisprudência e prática. 4. Ed. São Paulo: Saraiva, 2018.
WILLIANS, Donald. Bomb Safety and Security: The Manager´s Guide. Collaborative Publications (C-Pubs), 2016.VISACRO, Alessandro. A Guerra na Era da Informação. São Paulo: Contexto, 2018.
1Acadêmico do curso de Ciências da Computação pela Fundação Centro de Análise, Pesquisa e Inovação Tecnológica – FUCAPI. E-mail: luizhenrique.mmn@gmail.com. ORCID: https://orcid.org/0009-0008-5767-3528.
2Professor Co-Orientador, Especialista em Educação do Campo pelo Instituto Federal do Amazonas e Metodologia do Ensino Superior pelo Instituto Fase do Amazonas. Graduado em Geografia pelo Centro Universitário do Norte – UNINORTE. E-mail: cms.1978@hotmail.com. ORCID: https://orcid.org/0000-0002-3923-8839.
3Professor Orientador do curso de Ciências da Computação pela Fundação Centro de Análise, Pesquisa e Inovação Tecnológica – FUCAPI.