Predictive Policing and Artificial Intelligence:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Milton
Taylor & Francis Group
2021
|
| Series: | Routledge Frontiers of Criminal Justice Ser
|
| Subjects: | |
| Online Access: | HWR01 |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (331 Seiten) |
| ISBN: | 9780429560385 |
Staff View
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| 505 | 8 | |a Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Dedication -- Table of contents -- Illustrations -- Foreword -- Contributors -- Introduction -- Two extremes -- A challenging environment for police forces -- What is predictive policing? -- The 'predictive' part -- Environmental criminology and crime science -- Artificial intelligence -- The 'policing' part -- AI in policing -- Structure of book -- Part I: Bias and big data -- Part II: Police accountability and human rights -- References -- Part I Bias and Big Data -- Chapter 1 The future of AI in policing: Exploring the sociotechnical imaginaries -- Introduction -- 1. Sociotechnical imaginaries -- 2. The benefits and risks of AI for society -- Definition of AI -- The benefits of AI -- The risks of AI -- Technical limitations -- Data-driven biases -- Trust -- 3. Using AI in policing -- A utopian view -- A social science view -- Assumptions -- Evaluation -- Accountability -- A data science view -- A civil rights community view -- Conclusion -- Notes -- References -- Chapter 2 Predictive policing through risk assessment -- Introduction -- Projected benefits of predictive policing with individual risk -- Examples of predictive policing tools with individual risk -- Contentious issues with individual risk prediction -- Entry points for biases in predictive policing algorithms -- Label bias -- Feature selection -- Sample bias -- Feedback loop -- Future prospects for predictive policing -- Conclusions -- References -- Chapter 3 Policing, AI and choice architecture -- Introduction -- The ubiquity of choice architecture -- Policing and choice architecture -- AI and choice architecture -- AI as a product of choice architects -- AI technologies as choice architects -- Choice architects within police organisations -- Conclusion -- References | |
| 505 | 8 | |a Chapter 4 What big data in health care can teach us about predictive policing -- Introduction -- Part I -- Predictive analytics in policing and health care -- Predictive policing -- Health care -- Part II -- The professions in dialogue -- Practitioners -- Role disruption -- Automation bias and discretion -- Policymakers -- The duty of explanation -- Transparency and trade secrets -- Scarcity and the inevitability of distributional choices -- The polity -- Bias and equality -- Privacy -- Conclusion -- Acknowledgement -- Notes -- References -- Chapter 5 Artificial intelligence and online extremism: Challenges and opportunities -- Introduction -- An overview of existing approaches -- Analysis -- Detection -- Prediction -- Challenges -- Defining radicalisation -- Data collection, verification and publication -- Noisy data (false positives) -- Biases -- Incompleteness -- Heterogeneity (variety of content) -- Irreproducibility -- Research methodologies -- Lack of comparison against a control group -- Lack of comparison across approaches -- Lack of cooperation across research fields -- Adaptation of extremist groups -- Ethics and conflicts in legislation -- Opportunities -- Collaboration across research disciplines and organisations -- Creation of reliable datasets to study radicalisation -- Comparative studies -- Contextual adaptation of technological solutions -- Better integration of humans and technology -- Ethical vigilance -- Conclusions -- References -- Chapter 6 Predictive policing and criminal law -- Introduction -- Part I: Crime prevention and law enforcement -- A. Rational offenders and the expected benefits and costs of crime -- B. Punishment-focused deterrence -- C. Police-focused deterrence -- D. Long-term and short-term deterrence -- E. Real-time policing and enforcing the criminal law -- Part II: Machine predictions and policing | |
| 505 | 8 | |a A. Machine learning: a brief overview -- B. Place-based predictions -- C. Person-based predictive policing -- D. Real-time situational awareness technologies -- Part III: Real-time policing and crime prevention -- A. Inchoate and corollary crimes -- B. Precommitment and credible law enforcement policies -- C. Real-time policing, salient signals and deterrence -- Myopic offenders -- Self-control problems and time-inconsistent misconduct -- Perceptual deterrence and 'erroneous crimes' -- Learning from crime and serial offenders -- D. Real-time intervention -- The projection bias and hot-state crimes -- Risky crimes -- Part IV: The social costs of relying on machine predictions in policing -- A. Fairness and accuracy -- B. Machine predictions and indirect, non-transparent deterrence -- C. Switching to more serious crimes under a proactive predictive policing regime -- D. Machine predictions and police judgements -- E. The costs of proactive deterrence policies -- Conclusion -- References -- Part II Police accountability and human rights -- Chapter 7 Accountability and indeterminacy in predictive policing -- Introduction -- Police, accountability, transparency and reform -- Three accountabilities -- Algorithmic indeterminacy -- Towards a police accountability frame for the age of AI -- References -- Chapter 8 Machine learning predictive algorithms and the policing of future crimes: Governance and oversight -- Introduction -- Functions of the police in England and Wales under the common law -- 'Austerity AI' and the problem of prioritisation -- 'In accordance with law' -- Discretion in police decision-making -- Impact on rights -- Safeguards, governance and oversight -- Conclusion -- Notes -- References -- Chapter 9 'Algorithmic impropriety' in UK policing contexts: A developing narrative? -- Introduction -- Algorithms in the UK public sector | |
| 505 | 8 | |a The Gangs Matrix case study -- The West Midlands case study -- Legal points on algorithmic or predictive policing tools -- Data scope issues -- Process issues -- Issues of human rights impacts -- Conclusions -- References -- Chapter 10 Big data policing: Governing the machines? -- Introduction -- The problem of governance -- The problem of privacy -- The problem of bias -- Conclusion -- References -- Chapter 11 Decision-making: Using technology to enhance learning in police officers -- Introduction -- Artificial intelligence -- The demands of modern-day policing -- Training and upskilling the next generation of officers -- Contextualising learning -- Developing a personalised reflective learning environment for policing using technology -- Policing exemplar #1 -- Created immersive learning environments -- Policing exemplar #2 -- Responsive immersive learning environment: application of the decision-making framework -- Policing exemplar #3 -- Summary and future directions -- References -- Conclusion -- References -- Index | |
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| contents | Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Dedication -- Table of contents -- Illustrations -- Foreword -- Contributors -- Introduction -- Two extremes -- A challenging environment for police forces -- What is predictive policing? -- The 'predictive' part -- Environmental criminology and crime science -- Artificial intelligence -- The 'policing' part -- AI in policing -- Structure of book -- Part I: Bias and big data -- Part II: Police accountability and human rights -- References -- Part I Bias and Big Data -- Chapter 1 The future of AI in policing: Exploring the sociotechnical imaginaries -- Introduction -- 1. Sociotechnical imaginaries -- 2. The benefits and risks of AI for society -- Definition of AI -- The benefits of AI -- The risks of AI -- Technical limitations -- Data-driven biases -- Trust -- 3. Using AI in policing -- A utopian view -- A social science view -- Assumptions -- Evaluation -- Accountability -- A data science view -- A civil rights community view -- Conclusion -- Notes -- References -- Chapter 2 Predictive policing through risk assessment -- Introduction -- Projected benefits of predictive policing with individual risk -- Examples of predictive policing tools with individual risk -- Contentious issues with individual risk prediction -- Entry points for biases in predictive policing algorithms -- Label bias -- Feature selection -- Sample bias -- Feedback loop -- Future prospects for predictive policing -- Conclusions -- References -- Chapter 3 Policing, AI and choice architecture -- Introduction -- The ubiquity of choice architecture -- Policing and choice architecture -- AI and choice architecture -- AI as a product of choice architects -- AI technologies as choice architects -- Choice architects within police organisations -- Conclusion -- References Chapter 4 What big data in health care can teach us about predictive policing -- Introduction -- Part I -- Predictive analytics in policing and health care -- Predictive policing -- Health care -- Part II -- The professions in dialogue -- Practitioners -- Role disruption -- Automation bias and discretion -- Policymakers -- The duty of explanation -- Transparency and trade secrets -- Scarcity and the inevitability of distributional choices -- The polity -- Bias and equality -- Privacy -- Conclusion -- Acknowledgement -- Notes -- References -- Chapter 5 Artificial intelligence and online extremism: Challenges and opportunities -- Introduction -- An overview of existing approaches -- Analysis -- Detection -- Prediction -- Challenges -- Defining radicalisation -- Data collection, verification and publication -- Noisy data (false positives) -- Biases -- Incompleteness -- Heterogeneity (variety of content) -- Irreproducibility -- Research methodologies -- Lack of comparison against a control group -- Lack of comparison across approaches -- Lack of cooperation across research fields -- Adaptation of extremist groups -- Ethics and conflicts in legislation -- Opportunities -- Collaboration across research disciplines and organisations -- Creation of reliable datasets to study radicalisation -- Comparative studies -- Contextual adaptation of technological solutions -- Better integration of humans and technology -- Ethical vigilance -- Conclusions -- References -- Chapter 6 Predictive policing and criminal law -- Introduction -- Part I: Crime prevention and law enforcement -- A. Rational offenders and the expected benefits and costs of crime -- B. Punishment-focused deterrence -- C. Police-focused deterrence -- D. Long-term and short-term deterrence -- E. Real-time policing and enforcing the criminal law -- Part II: Machine predictions and policing A. Machine learning: a brief overview -- B. Place-based predictions -- C. Person-based predictive policing -- D. Real-time situational awareness technologies -- Part III: Real-time policing and crime prevention -- A. Inchoate and corollary crimes -- B. Precommitment and credible law enforcement policies -- C. Real-time policing, salient signals and deterrence -- Myopic offenders -- Self-control problems and time-inconsistent misconduct -- Perceptual deterrence and 'erroneous crimes' -- Learning from crime and serial offenders -- D. Real-time intervention -- The projection bias and hot-state crimes -- Risky crimes -- Part IV: The social costs of relying on machine predictions in policing -- A. Fairness and accuracy -- B. Machine predictions and indirect, non-transparent deterrence -- C. Switching to more serious crimes under a proactive predictive policing regime -- D. Machine predictions and police judgements -- E. The costs of proactive deterrence policies -- Conclusion -- References -- Part II Police accountability and human rights -- Chapter 7 Accountability and indeterminacy in predictive policing -- Introduction -- Police, accountability, transparency and reform -- Three accountabilities -- Algorithmic indeterminacy -- Towards a police accountability frame for the age of AI -- References -- Chapter 8 Machine learning predictive algorithms and the policing of future crimes: Governance and oversight -- Introduction -- Functions of the police in England and Wales under the common law -- 'Austerity AI' and the problem of prioritisation -- 'In accordance with law' -- Discretion in police decision-making -- Impact on rights -- Safeguards, governance and oversight -- Conclusion -- Notes -- References -- Chapter 9 'Algorithmic impropriety' in UK policing contexts: A developing narrative? -- Introduction -- Algorithms in the UK public sector The Gangs Matrix case study -- The West Midlands case study -- Legal points on algorithmic or predictive policing tools -- Data scope issues -- Process issues -- Issues of human rights impacts -- Conclusions -- References -- Chapter 10 Big data policing: Governing the machines? -- Introduction -- The problem of governance -- The problem of privacy -- The problem of bias -- Conclusion -- References -- Chapter 11 Decision-making: Using technology to enhance learning in police officers -- Introduction -- Artificial intelligence -- The demands of modern-day policing -- Training and upskilling the next generation of officers -- Contextualising learning -- Developing a personalised reflective learning environment for policing using technology -- Policing exemplar #1 -- Created immersive learning environments -- Policing exemplar #2 -- Responsive immersive learning environment: application of the decision-making framework -- Policing exemplar #3 -- Summary and future directions -- References -- Conclusion -- References -- Index |
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Real-time intervention -- The projection bias and hot-state crimes -- Risky crimes -- Part IV: The social costs of relying on machine predictions in policing -- A. Fairness and accuracy -- B. Machine predictions and indirect, non-transparent deterrence -- C. Switching to more serious crimes under a proactive predictive policing regime -- D. Machine predictions and police judgements -- E. 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| index_date | 2024-07-03T18:58:07Z |
| indexdate | 2024-07-10T09:19:26Z |
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| spelling | McDaniel, John Verfasser aut Predictive Policing and Artificial Intelligence Milton Taylor & Francis Group 2021 ©2021 1 Online-Ressource (331 Seiten) txt rdacontent c rdamedia cr rdacarrier Routledge Frontiers of Criminal Justice Ser Description based on publisher supplied metadata and other sources Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Dedication -- Table of contents -- Illustrations -- Foreword -- Contributors -- Introduction -- Two extremes -- A challenging environment for police forces -- What is predictive policing? -- The 'predictive' part -- Environmental criminology and crime science -- Artificial intelligence -- The 'policing' part -- AI in policing -- Structure of book -- Part I: Bias and big data -- Part II: Police accountability and human rights -- References -- Part I Bias and Big Data -- Chapter 1 The future of AI in policing: Exploring the sociotechnical imaginaries -- Introduction -- 1. Sociotechnical imaginaries -- 2. The benefits and risks of AI for society -- Definition of AI -- The benefits of AI -- The risks of AI -- Technical limitations -- Data-driven biases -- Trust -- 3. Using AI in policing -- A utopian view -- A social science view -- Assumptions -- Evaluation -- Accountability -- A data science view -- A civil rights community view -- Conclusion -- Notes -- References -- Chapter 2 Predictive policing through risk assessment -- Introduction -- Projected benefits of predictive policing with individual risk -- Examples of predictive policing tools with individual risk -- Contentious issues with individual risk prediction -- Entry points for biases in predictive policing algorithms -- Label bias -- Feature selection -- Sample bias -- Feedback loop -- Future prospects for predictive policing -- Conclusions -- References -- Chapter 3 Policing, AI and choice architecture -- Introduction -- The ubiquity of choice architecture -- Policing and choice architecture -- AI and choice architecture -- AI as a product of choice architects -- AI technologies as choice architects -- Choice architects within police organisations -- Conclusion -- References Chapter 4 What big data in health care can teach us about predictive policing -- Introduction -- Part I -- Predictive analytics in policing and health care -- Predictive policing -- Health care -- Part II -- The professions in dialogue -- Practitioners -- Role disruption -- Automation bias and discretion -- Policymakers -- The duty of explanation -- Transparency and trade secrets -- Scarcity and the inevitability of distributional choices -- The polity -- Bias and equality -- Privacy -- Conclusion -- Acknowledgement -- Notes -- References -- Chapter 5 Artificial intelligence and online extremism: Challenges and opportunities -- Introduction -- An overview of existing approaches -- Analysis -- Detection -- Prediction -- Challenges -- Defining radicalisation -- Data collection, verification and publication -- Noisy data (false positives) -- Biases -- Incompleteness -- Heterogeneity (variety of content) -- Irreproducibility -- Research methodologies -- Lack of comparison against a control group -- Lack of comparison across approaches -- Lack of cooperation across research fields -- Adaptation of extremist groups -- Ethics and conflicts in legislation -- Opportunities -- Collaboration across research disciplines and organisations -- Creation of reliable datasets to study radicalisation -- Comparative studies -- Contextual adaptation of technological solutions -- Better integration of humans and technology -- Ethical vigilance -- Conclusions -- References -- Chapter 6 Predictive policing and criminal law -- Introduction -- Part I: Crime prevention and law enforcement -- A. Rational offenders and the expected benefits and costs of crime -- B. Punishment-focused deterrence -- C. Police-focused deterrence -- D. Long-term and short-term deterrence -- E. Real-time policing and enforcing the criminal law -- Part II: Machine predictions and policing A. Machine learning: a brief overview -- B. Place-based predictions -- C. Person-based predictive policing -- D. Real-time situational awareness technologies -- Part III: Real-time policing and crime prevention -- A. Inchoate and corollary crimes -- B. Precommitment and credible law enforcement policies -- C. Real-time policing, salient signals and deterrence -- Myopic offenders -- Self-control problems and time-inconsistent misconduct -- Perceptual deterrence and 'erroneous crimes' -- Learning from crime and serial offenders -- D. Real-time intervention -- The projection bias and hot-state crimes -- Risky crimes -- Part IV: The social costs of relying on machine predictions in policing -- A. Fairness and accuracy -- B. Machine predictions and indirect, non-transparent deterrence -- C. Switching to more serious crimes under a proactive predictive policing regime -- D. Machine predictions and police judgements -- E. The costs of proactive deterrence policies -- Conclusion -- References -- Part II Police accountability and human rights -- Chapter 7 Accountability and indeterminacy in predictive policing -- Introduction -- Police, accountability, transparency and reform -- Three accountabilities -- Algorithmic indeterminacy -- Towards a police accountability frame for the age of AI -- References -- Chapter 8 Machine learning predictive algorithms and the policing of future crimes: Governance and oversight -- Introduction -- Functions of the police in England and Wales under the common law -- 'Austerity AI' and the problem of prioritisation -- 'In accordance with law' -- Discretion in police decision-making -- Impact on rights -- Safeguards, governance and oversight -- Conclusion -- Notes -- References -- Chapter 9 'Algorithmic impropriety' in UK policing contexts: A developing narrative? -- Introduction -- Algorithms in the UK public sector The Gangs Matrix case study -- The West Midlands case study -- Legal points on algorithmic or predictive policing tools -- Data scope issues -- Process issues -- Issues of human rights impacts -- Conclusions -- References -- Chapter 10 Big data policing: Governing the machines? -- Introduction -- The problem of governance -- The problem of privacy -- The problem of bias -- Conclusion -- References -- Chapter 11 Decision-making: Using technology to enhance learning in police officers -- Introduction -- Artificial intelligence -- The demands of modern-day policing -- Training and upskilling the next generation of officers -- Contextualising learning -- Developing a personalised reflective learning environment for policing using technology -- Policing exemplar #1 -- Created immersive learning environments -- Policing exemplar #2 -- Responsive immersive learning environment: application of the decision-making framework -- Policing exemplar #3 -- Summary and future directions -- References -- Conclusion -- References -- Index Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Kriminalität (DE-588)4033178-7 gnd rswk-swf Polizei (DE-588)4046595-0 gnd rswk-swf Bekämpfung (DE-588)4112701-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Polizei (DE-588)4046595-0 s Kriminalität (DE-588)4033178-7 s Bekämpfung (DE-588)4112701-8 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Pease, Ken Sonstige oth Erscheint auch als Druck-Ausgabe McDaniel, John Predictive Policing and Artificial Intelligence Milton : Taylor & Francis Group,c2021 9780367210984 |
| spellingShingle | McDaniel, John Predictive Policing and Artificial Intelligence Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Dedication -- Table of contents -- Illustrations -- Foreword -- Contributors -- Introduction -- Two extremes -- A challenging environment for police forces -- What is predictive policing? -- The 'predictive' part -- Environmental criminology and crime science -- Artificial intelligence -- The 'policing' part -- AI in policing -- Structure of book -- Part I: Bias and big data -- Part II: Police accountability and human rights -- References -- Part I Bias and Big Data -- Chapter 1 The future of AI in policing: Exploring the sociotechnical imaginaries -- Introduction -- 1. Sociotechnical imaginaries -- 2. The benefits and risks of AI for society -- Definition of AI -- The benefits of AI -- The risks of AI -- Technical limitations -- Data-driven biases -- Trust -- 3. Using AI in policing -- A utopian view -- A social science view -- Assumptions -- Evaluation -- Accountability -- A data science view -- A civil rights community view -- Conclusion -- Notes -- References -- Chapter 2 Predictive policing through risk assessment -- Introduction -- Projected benefits of predictive policing with individual risk -- Examples of predictive policing tools with individual risk -- Contentious issues with individual risk prediction -- Entry points for biases in predictive policing algorithms -- Label bias -- Feature selection -- Sample bias -- Feedback loop -- Future prospects for predictive policing -- Conclusions -- References -- Chapter 3 Policing, AI and choice architecture -- Introduction -- The ubiquity of choice architecture -- Policing and choice architecture -- AI and choice architecture -- AI as a product of choice architects -- AI technologies as choice architects -- Choice architects within police organisations -- Conclusion -- References Chapter 4 What big data in health care can teach us about predictive policing -- Introduction -- Part I -- Predictive analytics in policing and health care -- Predictive policing -- Health care -- Part II -- The professions in dialogue -- Practitioners -- Role disruption -- Automation bias and discretion -- Policymakers -- The duty of explanation -- Transparency and trade secrets -- Scarcity and the inevitability of distributional choices -- The polity -- Bias and equality -- Privacy -- Conclusion -- Acknowledgement -- Notes -- References -- Chapter 5 Artificial intelligence and online extremism: Challenges and opportunities -- Introduction -- An overview of existing approaches -- Analysis -- Detection -- Prediction -- Challenges -- Defining radicalisation -- Data collection, verification and publication -- Noisy data (false positives) -- Biases -- Incompleteness -- Heterogeneity (variety of content) -- Irreproducibility -- Research methodologies -- Lack of comparison against a control group -- Lack of comparison across approaches -- Lack of cooperation across research fields -- Adaptation of extremist groups -- Ethics and conflicts in legislation -- Opportunities -- Collaboration across research disciplines and organisations -- Creation of reliable datasets to study radicalisation -- Comparative studies -- Contextual adaptation of technological solutions -- Better integration of humans and technology -- Ethical vigilance -- Conclusions -- References -- Chapter 6 Predictive policing and criminal law -- Introduction -- Part I: Crime prevention and law enforcement -- A. Rational offenders and the expected benefits and costs of crime -- B. Punishment-focused deterrence -- C. Police-focused deterrence -- D. Long-term and short-term deterrence -- E. Real-time policing and enforcing the criminal law -- Part II: Machine predictions and policing A. Machine learning: a brief overview -- B. Place-based predictions -- C. Person-based predictive policing -- D. Real-time situational awareness technologies -- Part III: Real-time policing and crime prevention -- A. Inchoate and corollary crimes -- B. Precommitment and credible law enforcement policies -- C. Real-time policing, salient signals and deterrence -- Myopic offenders -- Self-control problems and time-inconsistent misconduct -- Perceptual deterrence and 'erroneous crimes' -- Learning from crime and serial offenders -- D. Real-time intervention -- The projection bias and hot-state crimes -- Risky crimes -- Part IV: The social costs of relying on machine predictions in policing -- A. Fairness and accuracy -- B. Machine predictions and indirect, non-transparent deterrence -- C. Switching to more serious crimes under a proactive predictive policing regime -- D. Machine predictions and police judgements -- E. The costs of proactive deterrence policies -- Conclusion -- References -- Part II Police accountability and human rights -- Chapter 7 Accountability and indeterminacy in predictive policing -- Introduction -- Police, accountability, transparency and reform -- Three accountabilities -- Algorithmic indeterminacy -- Towards a police accountability frame for the age of AI -- References -- Chapter 8 Machine learning predictive algorithms and the policing of future crimes: Governance and oversight -- Introduction -- Functions of the police in England and Wales under the common law -- 'Austerity AI' and the problem of prioritisation -- 'In accordance with law' -- Discretion in police decision-making -- Impact on rights -- Safeguards, governance and oversight -- Conclusion -- Notes -- References -- Chapter 9 'Algorithmic impropriety' in UK policing contexts: A developing narrative? -- Introduction -- Algorithms in the UK public sector The Gangs Matrix case study -- The West Midlands case study -- Legal points on algorithmic or predictive policing tools -- Data scope issues -- Process issues -- Issues of human rights impacts -- Conclusions -- References -- Chapter 10 Big data policing: Governing the machines? -- Introduction -- The problem of governance -- The problem of privacy -- The problem of bias -- Conclusion -- References -- Chapter 11 Decision-making: Using technology to enhance learning in police officers -- Introduction -- Artificial intelligence -- The demands of modern-day policing -- Training and upskilling the next generation of officers -- Contextualising learning -- Developing a personalised reflective learning environment for policing using technology -- Policing exemplar #1 -- Created immersive learning environments -- Policing exemplar #2 -- Responsive immersive learning environment: application of the decision-making framework -- Policing exemplar #3 -- Summary and future directions -- References -- Conclusion -- References -- Index Künstliche Intelligenz (DE-588)4033447-8 gnd Kriminalität (DE-588)4033178-7 gnd Polizei (DE-588)4046595-0 gnd Bekämpfung (DE-588)4112701-8 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4033178-7 (DE-588)4046595-0 (DE-588)4112701-8 (DE-588)4143413-4 |
| title | Predictive Policing and Artificial Intelligence |
| title_auth | Predictive Policing and Artificial Intelligence |
| title_exact_search | Predictive Policing and Artificial Intelligence |
| title_exact_search_txtP | Predictive Policing and Artificial Intelligence |
| title_full | Predictive Policing and Artificial Intelligence |
| title_fullStr | Predictive Policing and Artificial Intelligence |
| title_full_unstemmed | Predictive Policing and Artificial Intelligence |
| title_short | Predictive Policing and Artificial Intelligence |
| title_sort | predictive policing and artificial intelligence |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Kriminalität (DE-588)4033178-7 gnd Polizei (DE-588)4046595-0 gnd Bekämpfung (DE-588)4112701-8 gnd |
| topic_facet | Künstliche Intelligenz Kriminalität Polizei Bekämpfung Aufsatzsammlung |
| work_keys_str_mv | AT mcdanieljohn predictivepolicingandartificialintelligence AT peaseken predictivepolicingandartificialintelligence |