1.
Rani, Meena
Impacts and ethics of using Artificial Intelligence (AI) by the Indian Police Journal Article
In: Public Administration and Policy, vol. 27, no. 2, pp. 182–192, 2024, ISSN: 2517-679X, (Publisher: Emerald Publishing Limited).
@article{rani_impacts_2024,
title = {Impacts and ethics of using Artificial Intelligence (AI) by the Indian Police},
author = {Meena Rani},
url = {https://www.emerald.com/insight/content/doi/10.1108/pap-06-2023-0081/full/html},
doi = {10.1108/PAP-06-2023-0081},
issn = {2517-679X},
year = {2024},
date = {2024-08-01},
urldate = {2024-11-22},
journal = {Public Administration and Policy},
volume = {27},
number = {2},
pages = {182–192},
abstract = {The paper aims to examine the impacts and ethics of utilizing Artificial Intelligence (AI) in Indian policing. It explores both the positive and negative consequences of using AI, as well as the ethical considerations that have be taken into account.,This study is based on secondary sources of information, such as national and international reports, journal articles, and institutional websites that discuss the use of AI technology by the police in India.,AI has proven to be effective in policing, from preventing crime to identifying criminals, by detecting potential crimes in advance with fewer resources and in more areas. In India, the police use AI technology not only for facial recognition but also for crime mapping, analysis, and building blocks. However, factors such as caste, religion, language, and gender continue to cause conflict. India has shown a strong interest in using AI technology for policing, and wishes to accelerate its implementation in various policing contexts, including law and order. This paper calls for an assessment of the complexities and uncertainties brought about by new technologies in policing with ethical considerations.,This paper can provide valuable insights for policy-makers, academics, and practitioners engaged in discussions and debates concerning the ethical considerations associated with the adoption of AI tools in policing practices.},
note = {Publisher: Emerald Publishing Limited},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The paper aims to examine the impacts and ethics of utilizing Artificial Intelligence (AI) in Indian policing. It explores both the positive and negative consequences of using AI, as well as the ethical considerations that have be taken into account.,This study is based on secondary sources of information, such as national and international reports, journal articles, and institutional websites that discuss the use of AI technology by the police in India.,AI has proven to be effective in policing, from preventing crime to identifying criminals, by detecting potential crimes in advance with fewer resources and in more areas. In India, the police use AI technology not only for facial recognition but also for crime mapping, analysis, and building blocks. However, factors such as caste, religion, language, and gender continue to cause conflict. India has shown a strong interest in using AI technology for policing, and wishes to accelerate its implementation in various policing contexts, including law and order. This paper calls for an assessment of the complexities and uncertainties brought about by new technologies in policing with ethical considerations.,This paper can provide valuable insights for policy-makers, academics, and practitioners engaged in discussions and debates concerning the ethical considerations associated with the adoption of AI tools in policing practices.
2.
Ferguson, Andrew Guthrie
Generative Suspicion and the Risks of AI-Assisted Police Reports Miscellaneous
2024.
@misc{ferguson_generative_2024,
title = {Generative Suspicion and the Risks of AI-Assisted Police Reports},
author = {Andrew Guthrie Ferguson},
url = {https://papers.ssrn.com/abstract=4897632},
year = {2024},
date = {2024-07-01},
urldate = {2024-11-22},
publisher = {Social Science Research Network},
address = {Rochester, NY},
abstract = {<p><span>Police reports play a central role in the criminal justice system. Many times, police reports exist as the only official memorialization of what happen},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
<p><span>Police reports play a central role in the criminal justice system. Many times, police reports exist as the only official memorialization of what happen
3.
Donatz-Fest, I. C.
Values? Camera? Action! An ethnography of an AI camera system used by the Netherlands Police Journal Article
In: Policing and Society, vol. 0, no. 0, pp. 1–18, 2024, ISSN: 1043-9463, (Publisher: Routledge _eprint: https://doi.org/10.1080/10439463.2024.2370939).
@article{donatz-fest_values_2024,
title = {Values? Camera? Action! An ethnography of an AI camera system used by the Netherlands Police},
author = {I. C. Donatz-Fest},
url = {https://doi.org/10.1080/10439463.2024.2370939},
doi = {10.1080/10439463.2024.2370939},
issn = {1043-9463},
year = {2024},
date = {2024-07-01},
urldate = {2024-11-22},
journal = {Policing and Society},
volume = {0},
number = {0},
pages = {1–18},
abstract = {Police departments around the world implement algorithmic systems to enhance various policing tasks. Ensuring such innovations take place responsibly – with public values upheld – is essential for public organisations. This paper analyses how public values are safeguarded in the case of MONOcam, an algorithmic camera system designed and used by the Netherlands police. The system employs artificial intelligence to detect whether car drivers are holding a mobile device. MONOcam can be considered a good example of value-sensitive design; many measures were taken to safeguard public values in this algorithmic system. In pursuit of responsible implementation of algorithms, most calls and literature focus on such value-sensitive design. Less attention is paid to what happens beyond design. Building on 120+ hours of ethnographic observations as well as informal conversations and three semi-structured interviews, this research shows that public values deemed safeguarded in design are re-negotiated as the system is implemented and used in practice. These findings led to direct impact, as MONOcam was improved in response. This paper thus highlights that algorithmic system design is often based on an ideal world, but it is in the complexities and fuzzy realities of everyday professional routines and sociomaterial reality that these systems are enacted, and public values are renegotiated in the use of algorithms. While value-sensitive design is important, this paper shows that it offers no guarantees for safeguarding public values in practice.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/10439463.2024.2370939},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Police departments around the world implement algorithmic systems to enhance various policing tasks. Ensuring such innovations take place responsibly – with public values upheld – is essential for public organisations. This paper analyses how public values are safeguarded in the case of MONOcam, an algorithmic camera system designed and used by the Netherlands police. The system employs artificial intelligence to detect whether car drivers are holding a mobile device. MONOcam can be considered a good example of value-sensitive design; many measures were taken to safeguard public values in this algorithmic system. In pursuit of responsible implementation of algorithms, most calls and literature focus on such value-sensitive design. Less attention is paid to what happens beyond design. Building on 120+ hours of ethnographic observations as well as informal conversations and three semi-structured interviews, this research shows that public values deemed safeguarded in design are re-negotiated as the system is implemented and used in practice. These findings led to direct impact, as MONOcam was improved in response. This paper thus highlights that algorithmic system design is often based on an ideal world, but it is in the complexities and fuzzy realities of everyday professional routines and sociomaterial reality that these systems are enacted, and public values are renegotiated in the use of algorithms. While value-sensitive design is important, this paper shows that it offers no guarantees for safeguarding public values in practice.
4.
Bell, Raoul; Menne, Nicola Marie; Mayer, Carolin; Buchner, Axel
On the advantages of using AI-generated images of filler faces for creating fair lineups Journal Article
In: Scientific Reports, vol. 14, no. 1, pp. 12304, 2024, ISSN: 2045-2322, (Publisher: Nature Publishing Group).
@article{bell_advantages_2024,
title = {On the advantages of using AI-generated images of filler faces for creating fair lineups},
author = {Raoul Bell and Nicola Marie Menne and Carolin Mayer and Axel Buchner},
url = {https://www.nature.com/articles/s41598-024-63004-z},
doi = {10.1038/s41598-024-63004-z},
issn = {2045-2322},
year = {2024},
date = {2024-05-01},
urldate = {2024-11-22},
journal = {Scientific Reports},
volume = {14},
number = {1},
pages = {12304},
abstract = {Recent advances in artificial intelligence (AI) enable the generation of realistic facial images that can be used in police lineups. The use of AI image generation offers pragmatic advantages in that it allows practitioners to generate filler images directly from the description of the culprit using text-to-image generation, avoids the violation of identity rights of natural persons who are not suspects and eliminates the constraints of being bound to a database with a limited set of photographs. However, the risk exists that using AI-generated filler images provokes more biased selection of the suspect if eyewitnesses are able to distinguish AI-generated filler images from the photograph of the suspect’s face. Using a model-based analysis, we compared biased suspect selection directly between lineups with AI-generated filler images and lineups with database-derived filler photographs. The results show that the lineups with AI-generated filler images were perfectly fair and, in fact, led to less biased suspect selection than the lineups with database-derived filler photographs used in previous experiments. These results are encouraging with regard to the potential of AI image generation for constructing fair lineups which should inspire more systematic research on the feasibility of adopting AI technology in forensic settings.},
note = {Publisher: Nature Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recent advances in artificial intelligence (AI) enable the generation of realistic facial images that can be used in police lineups. The use of AI image generation offers pragmatic advantages in that it allows practitioners to generate filler images directly from the description of the culprit using text-to-image generation, avoids the violation of identity rights of natural persons who are not suspects and eliminates the constraints of being bound to a database with a limited set of photographs. However, the risk exists that using AI-generated filler images provokes more biased selection of the suspect if eyewitnesses are able to distinguish AI-generated filler images from the photograph of the suspect’s face. Using a model-based analysis, we compared biased suspect selection directly between lineups with AI-generated filler images and lineups with database-derived filler photographs. The results show that the lineups with AI-generated filler images were perfectly fair and, in fact, led to less biased suspect selection than the lineups with database-derived filler photographs used in previous experiments. These results are encouraging with regard to the potential of AI image generation for constructing fair lineups which should inspire more systematic research on the feasibility of adopting AI technology in forensic settings.
5.
Fernandez-Basso, Carlos; Gutiérrez-Batista, Karel; Gómez-Romero, Juan; Ruiz, M. Dolores; Martin-Bautista, Maria J.
An AI knowledge-based system for police assistance in crime investigation Journal Article
In: Expert Systems, vol. n/a, no. n/a, pp. e13524, 2024, ISSN: 1468-0394, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.13524).
@article{fernandez-basso_ai_2024,
title = {An AI knowledge-based system for police assistance in crime investigation},
author = {Carlos Fernandez-Basso and Karel Gutiérrez-Batista and Juan Gómez-Romero and M. Dolores Ruiz and Maria J. Martin-Bautista},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13524},
doi = {10.1111/exsy.13524},
issn = {1468-0394},
year = {2024},
date = {2024-01-01},
urldate = {2024-11-22},
journal = {Expert Systems},
volume = {n/a},
number = {n/a},
pages = {e13524},
abstract = {The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least mitigate the hard process of data collection, processing and analysis. The system is an early warning/early action system for crime investigation that supports law enforcement with different processes to collect and process data as well as having knowledge extraction tools. It helps to extract information during the investigation of a criminal case or even to detect possible criminal hotspots that may lead to further investigation or analysis of a criminal case Abu Al-Haija et al. (2022, Electronics, 11, 556). The functionality of the proposed system is illustrated through several examples using data collected from the dark web, which includes advertisements offering firearms-related products.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.13524},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least mitigate the hard process of data collection, processing and analysis. The system is an early warning/early action system for crime investigation that supports law enforcement with different processes to collect and process data as well as having knowledge extraction tools. It helps to extract information during the investigation of a criminal case or even to detect possible criminal hotspots that may lead to further investigation or analysis of a criminal case Abu Al-Haija et al. (2022, Electronics, 11, 556). The functionality of the proposed system is illustrated through several examples using data collected from the dark web, which includes advertisements offering firearms-related products.
6.
Sorell, Tom
AI-related data ethics oversight in UK policing Journal Article
In: Policing: A Journal of Policy and Practice, vol. 18, pp. paae016, 2024, ISSN: 1752-4520.
@article{sorell_ai-related_2024,
title = {AI-related data ethics oversight in UK policing},
author = {Tom Sorell},
url = {https://doi.org/10.1093/police/paae016},
doi = {10.1093/police/paae016},
issn = {1752-4520},
year = {2024},
date = {2024-01-01},
urldate = {2024-11-22},
journal = {Policing: A Journal of Policy and Practice},
volume = {18},
pages = {paae016},
abstract = {This paper considers the question of how police-related AI projects and data projects in general are normatively assessed in the UK. After locating data ethics in relation to policing ethics, I shall consider the workings of perhaps the leading regional data ethics committee in the UK. I go on to consider the approach of another committee that might in the future provide national data ethics advice for the police. Finally, I summarize the normative ethics frameworks in use in the two committees and their heavy reliance on the concepts of necessity and proportionality. I suggest that these concepts may have to be supplemented by systematic thinking about varieties of harm and the way in which severe harm may generate obligations to prevent it, where prevention may be assisted by AI models.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper considers the question of how police-related AI projects and data projects in general are normatively assessed in the UK. After locating data ethics in relation to policing ethics, I shall consider the workings of perhaps the leading regional data ethics committee in the UK. I go on to consider the approach of another committee that might in the future provide national data ethics advice for the police. Finally, I summarize the normative ethics frameworks in use in the two committees and their heavy reliance on the concepts of necessity and proportionality. I suggest that these concepts may have to be supplemented by systematic thinking about varieties of harm and the way in which severe harm may generate obligations to prevent it, where prevention may be assisted by AI models.
7.
Davies, Amanda; Krame, Ghaleb
Integrating body-worn cameras, drones, and AI: A framework for enhancing police readiness and response Journal Article
In: Policing: A Journal of Policy and Practice, vol. 17, pp. paad083, 2023, ISSN: 1752-4520.
@article{davies_integrating_2023,
title = {Integrating body-worn cameras, drones, and AI: A framework for enhancing police readiness and response},
author = {Amanda Davies and Ghaleb Krame},
url = {https://doi.org/10.1093/police/paad083},
doi = {10.1093/police/paad083},
issn = {1752-4520},
year = {2023},
date = {2023-01-01},
urldate = {2024-11-22},
journal = {Policing: A Journal of Policy and Practice},
volume = {17},
pages = {paad083},
abstract = {The combined use of body-worn cameras (BWCs), drones, and artificial intelligence (AI) within the context of policing represents a significant advancement in policing methodology. This article presents a comprehensive framework for (a) the integrated use of these technologies to promote real-time situational awareness, heightened evidence collection, enhanced officer and public safety, improved operational efficiency, cognizant of compliance with ethical and privacy standards; and (b) an evaluation approach to the combined technology application. Illustration of the framework application to historical high-profile events presents a unique lens to assess potential outcomes and advantages, fostering and informing on a comprehensive discussion on future policing policies. This examination aims to offer a practical approach for implementing a synergistic BWCs, drones, and AI framework to leverage policing initiatives.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The combined use of body-worn cameras (BWCs), drones, and artificial intelligence (AI) within the context of policing represents a significant advancement in policing methodology. This article presents a comprehensive framework for (a) the integrated use of these technologies to promote real-time situational awareness, heightened evidence collection, enhanced officer and public safety, improved operational efficiency, cognizant of compliance with ethical and privacy standards; and (b) an evaluation approach to the combined technology application. Illustration of the framework application to historical high-profile events presents a unique lens to assess potential outcomes and advantages, fostering and informing on a comprehensive discussion on future policing policies. This examination aims to offer a practical approach for implementing a synergistic BWCs, drones, and AI framework to leverage policing initiatives.
8.
Shamsi, Ahmed Surour Al; Davies, Amanda
Smart policing: Abu Dhabi police AI/GPS-based initiative to reduce heavy vehicle driver violations Journal Article
In: Policing: A Journal of Policy and Practice, vol. 16, no. 2, pp. 260–269, 2022, ISSN: 1752-4520.
@article{al_shamsi_smart_2022,
title = {Smart policing: Abu Dhabi police AI/GPS-based initiative to reduce heavy vehicle driver violations},
author = {Ahmed Surour Al Shamsi and Amanda Davies},
url = {https://doi.org/10.1093/police/paac011},
doi = {10.1093/police/paac011},
issn = {1752-4520},
year = {2022},
date = {2022-06-01},
urldate = {2024-11-22},
journal = {Policing: A Journal of Policy and Practice},
volume = {16},
number = {2},
pages = {260–269},
abstract = {One of the consequences of a rapidly developing 21st society is management of road traffic. Globally, road traffic collisions are among the most common incidents for which police are required to respond. The research presented in this article discusses an intervention strategy developed and applied by Abu Dhabi Police to reduce the impact of heavy vehicle driver violations and associated impact on collisions, deaths, and injuries in a period of 5 years up to and including April 2020. This article presents the findings from an impact evaluation of the Abu Dhabi Police Smart Traffic Centre Truck Permissions system that is supported by a complex multidimensional intelligent technology-based vehicle tracking system coupled with a driver violation penalty process. Analysis of data indicates a positive reduction rate in truck-caused accidents and resulting deaths and injuries during the post-intervention period. The research indicates effective policing strategies, which incorporate the affordances of smart technology have the potential to continuously improve road traffic management and by association the impact on the humanitarian and economic sustainability of a community and nation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
One of the consequences of a rapidly developing 21st society is management of road traffic. Globally, road traffic collisions are among the most common incidents for which police are required to respond. The research presented in this article discusses an intervention strategy developed and applied by Abu Dhabi Police to reduce the impact of heavy vehicle driver violations and associated impact on collisions, deaths, and injuries in a period of 5 years up to and including April 2020. This article presents the findings from an impact evaluation of the Abu Dhabi Police Smart Traffic Centre Truck Permissions system that is supported by a complex multidimensional intelligent technology-based vehicle tracking system coupled with a driver violation penalty process. Analysis of data indicates a positive reduction rate in truck-caused accidents and resulting deaths and injuries during the post-intervention period. The research indicates effective policing strategies, which incorporate the affordances of smart technology have the potential to continuously improve road traffic management and by association the impact on the humanitarian and economic sustainability of a community and nation.
9.
Sunde, Nina; Sunde, Inger Marie
Conceptualizing an AI-based Police Robot for Preventing Online Child Sexual Exploitation and Abuse: Journal Article
In: Nordic Journal of Studies in Policing, vol. 8, no. 2, pp. 1–21, 2021, (Publisher: Scandinavian University Press).
@article{sunde_conceptualizing_2021,
title = {Conceptualizing an AI-based Police Robot for Preventing Online Child Sexual Exploitation and Abuse:},
author = {Nina Sunde and Inger Marie Sunde},
url = {https://www.idunn.no/doi/10.18261/issn.2703-7045-2021-02-01},
doi = {10.18261/issn.2703-7045-2021-02-01},
year = {2021},
date = {2021-06-01},
urldate = {2024-11-22},
journal = {Nordic Journal of Studies in Policing},
volume = {8},
number = {2},
pages = {1–21},
abstract = {Child sexual exploitation and abuse (CSEA) needs more attention from a crime prevention perspective. This is the first in a two-part series about the PrevBOT concept, which is an automated tool supporting the police in preventing CSEA in online chat rooms. Part I presents the concept, its theoretical framework, and the technology. Equipped with technology for Authorship Analysis, the tool can identify problematic digital spaces unsafe for children. Given the advancements in machine learning algorithms, PrevBOT may provide predictions concerning age and gender behind online aliases engaged in sexualized speech with children and assist the police in identifying former CSEA offenders who resume the criminal activity online. Part II provides a legal analysis of issues relating to data protection, privacy, and fair trial.
Keywords
CSEA
crime prevention
machine learning
authorship analysis},
note = {Publisher: Scandinavian University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Child sexual exploitation and abuse (CSEA) needs more attention from a crime prevention perspective. This is the first in a two-part series about the PrevBOT concept, which is an automated tool supporting the police in preventing CSEA in online chat rooms. Part I presents the concept, its theoretical framework, and the technology. Equipped with technology for Authorship Analysis, the tool can identify problematic digital spaces unsafe for children. Given the advancements in machine learning algorithms, PrevBOT may provide predictions concerning age and gender behind online aliases engaged in sexualized speech with children and assist the police in identifying former CSEA offenders who resume the criminal activity online. Part II provides a legal analysis of issues relating to data protection, privacy, and fair trial.
Keywords
CSEA
crime prevention
machine learning
authorship analysis
Keywords
CSEA
crime prevention
machine learning
authorship analysis
10.
Noriega, Maria
In: Futures, vol. 117, pp. 102510, 2020, ISSN: 0016-3287.
@article{noriega_application_2020,
title = {The application of artificial intelligence in police interrogations: An analysis addressing the proposed effect AI has on racial and gender bias, cooperation, and false confessions},
author = {Maria Noriega},
url = {https://www.sciencedirect.com/science/article/pii/S0016328719303726},
doi = {10.1016/j.futures.2019.102510},
issn = {0016-3287},
year = {2020},
date = {2020-03-01},
urldate = {2024-11-22},
journal = {Futures},
volume = {117},
pages = {102510},
abstract = {Research presented in this study examines the potentiality of artificial intelligence as an interrogator within a police interrogation to promote a non-biased environment in an effort to mitigate the ongoing racial and gender divide in statistics regarding false confessions. Ideally, artificial intelligence supplementation may help promote the elicitation of non-coerced, voluntary confessions. This study suggests that the racial and gender bias influencing false confessions may be due to the two fold bias occurring within the interrogator-to-suspect dynamic, referenced in this study as “the Bias-Uncooperative Loop.” It argues that applying artificial intelligence within the interrogation room may minimize the two fold bias occurring in the dynamic. It suggests the potential for cooperation between the two parties can be conditioned by programmable similarity; whereby artificial intelligence can mimic the racial, ethnic and/or cultural similarities of the suspect in question. This is reflected in research in different arenas (not inclusive to interrogations) to have an effect on enhanced comfortability and cooperation with AI. This paper assumes similar results within interrogations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Research presented in this study examines the potentiality of artificial intelligence as an interrogator within a police interrogation to promote a non-biased environment in an effort to mitigate the ongoing racial and gender divide in statistics regarding false confessions. Ideally, artificial intelligence supplementation may help promote the elicitation of non-coerced, voluntary confessions. This study suggests that the racial and gender bias influencing false confessions may be due to the two fold bias occurring within the interrogator-to-suspect dynamic, referenced in this study as “the Bias-Uncooperative Loop.” It argues that applying artificial intelligence within the interrogation room may minimize the two fold bias occurring in the dynamic. It suggests the potential for cooperation between the two parties can be conditioned by programmable similarity; whereby artificial intelligence can mimic the racial, ethnic and/or cultural similarities of the suspect in question. This is reflected in research in different arenas (not inclusive to interrogations) to have an effect on enhanced comfortability and cooperation with AI. This paper assumes similar results within interrogations.