Predictive Policing and Artificial Intelligence:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Milton
Taylor & Francis Group
2021
|
| Schriftenreihe: | Routledge Frontiers of Criminal Justice Ser
|
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (331 Seiten) |
| ISBN: | 9780429560385 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV047698199 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 220120s2021 |||| o||u| ||||||eng d | ||
| 020 | |a 9780429560385 |9 978-0-429-56038-5 | ||
| 035 | |a (ZDB-30-PQE)EBC6452471 | ||
| 035 | |a (ZDB-30-PAD)EBC6452471 | ||
| 035 | |a (ZDB-89-EBL)EBL6452471 | ||
| 035 | |a (OCoLC)1231611823 | ||
| 035 | |a (DE-599)BVBBV047698199 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-2070s | ||
| 084 | |a PH 9160 |0 (DE-625)136535: |2 rvk | ||
| 100 | 1 | |a McDaniel, John |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Predictive Policing and Artificial Intelligence |
| 264 | 1 | |a Milton |b Taylor & Francis Group |c 2021 | |
| 264 | 4 | |c ©2021 | |
| 300 | |a 1 Online-Ressource (331 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Routledge Frontiers of Criminal Justice Ser | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 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 | |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kriminalität |0 (DE-588)4033178-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Polizei |0 (DE-588)4046595-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bekämpfung |0 (DE-588)4112701-8 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 689 | 0 | 0 | |a Polizei |0 (DE-588)4046595-0 |D s |
| 689 | 0 | 1 | |a Kriminalität |0 (DE-588)4033178-7 |D s |
| 689 | 0 | 2 | |a Bekämpfung |0 (DE-588)4112701-8 |D s |
| 689 | 0 | 3 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Pease, Ken |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a McDaniel, John |t Predictive Policing and Artificial Intelligence |d Milton : Taylor & Francis Group,c2021 |z 9780367210984 |
| 912 | |a ZDB-30-PQE | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033082164 | ||
| 966 | e | |u https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=6452471 |l HWR01 |p ZDB-30-PQE |q HWR_PDA_PQE |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804183186321702912 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | McDaniel, John |
| author_facet | McDaniel, John |
| author_role | aut |
| author_sort | McDaniel, John |
| author_variant | j m jm |
| building | Verbundindex |
| bvnumber | BV047698199 |
| classification_rvk | PH 9160 |
| collection | ZDB-30-PQE |
| 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 |
| ctrlnum | (ZDB-30-PQE)EBC6452471 (ZDB-30-PAD)EBC6452471 (ZDB-89-EBL)EBL6452471 (OCoLC)1231611823 (DE-599)BVBBV047698199 |
| discipline | Rechtswissenschaft |
| discipline_str_mv | Rechtswissenschaft |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>08688nmm a2200553zc 4500</leader><controlfield tag="001">BV047698199</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">220120s2021 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780429560385</subfield><subfield code="9">978-0-429-56038-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC6452471</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC6452471</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL6452471</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1231611823</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047698199</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-2070s</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">PH 9160</subfield><subfield code="0">(DE-625)136535:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">McDaniel, John</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predictive Policing and Artificial Intelligence</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Milton</subfield><subfield code="b">Taylor & Francis Group</subfield><subfield code="c">2021</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (331 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Routledge Frontiers of Criminal Justice Ser</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Kriminalität</subfield><subfield code="0">(DE-588)4033178-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Polizei</subfield><subfield code="0">(DE-588)4046595-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bekämpfung</subfield><subfield code="0">(DE-588)4112701-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Polizei</subfield><subfield code="0">(DE-588)4046595-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Kriminalität</subfield><subfield code="0">(DE-588)4033178-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Bekämpfung</subfield><subfield code="0">(DE-588)4112701-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pease, Ken</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">McDaniel, John</subfield><subfield code="t">Predictive Policing and Artificial Intelligence</subfield><subfield code="d">Milton : Taylor & Francis Group,c2021</subfield><subfield code="z">9780367210984</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033082164</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=6452471</subfield><subfield code="l">HWR01</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV047698199 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:58:07Z |
| indexdate | 2024-07-10T09:19:26Z |
| institution | BVB |
| isbn | 9780429560385 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033082164 |
| oclc_num | 1231611823 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (331 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Taylor & Francis Group |
| record_format | marc |
| series2 | Routledge Frontiers of Criminal Justice Ser |
| 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 |