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Aplikace strojového učení v systému trestního soudnictví USA: Kritická analýza obsahu hodnocení rizik recidivy COMPAS
dc.contributor.advisorŠpelda, Petr
dc.creatorBejarano Carbo, Maria Patricia
dc.date.accessioned2021-10-06T10:27:27Z
dc.date.available2021-10-06T10:27:27Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/20.500.11956/150387
dc.description.abstractArtificial intelligence and machine learning (AI/ML) models are increasingly utilised in every aspect of life and society due to their superhuman abilities to digest large amounts of data and find obscure patterns and correlations. One contentious area of this technological application is in the criminal justice system, where AI/ML is used as a recommendation or decision-making support tool. These applications are particularly popular in the United States of America (USA), the nation with the highest rate of incarceration and correctional budget, to aid in managing overcrowded and overspending facilities. Angwin et al.'s (2016) ground-breaking study found the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) model to be biased against Black defendants and sparked an influential academic debate around algorithmic bias and fairness. This study aims to fill the gap in the scholarship by focusing on the content of COMPAS's recidivism risk assessment questionnaire through a qualitative content analysis within the conceptual framework of Critical Race Theory (CRT). The findings presented in this research are twofold: (1) almost half of the COMPAS questions were opinion-based, thus reducing quantitative neutrality, and (2) there were significant proxy factors for race that...en_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Fakulta sociálních vědcs_CZ
dc.titleMachine learning applications in the United States criminal justice system: A critical content analysis of the COMPAS recidivism risk assessmenten_US
dc.typediplomová prácecs_CZ
dcterms.created2021
dcterms.dateAccepted2021-09-15
dc.description.departmentDepartment of Security Studiesen_US
dc.description.departmentKatedra bezpečnostních studiícs_CZ
dc.description.facultyFakulta sociálních vědcs_CZ
dc.description.facultyFaculty of Social Sciencesen_US
dc.identifier.repId236814
dc.title.translatedAplikace strojového učení v systému trestního soudnictví USA: Kritická analýza obsahu hodnocení rizik recidivy COMPAScs_CZ
dc.contributor.refereeFitzgerald, James
thesis.degree.nameMgr.
thesis.degree.levelnavazující magisterskécs_CZ
thesis.degree.disciplineInternational Master in Security, Intelligence and Strategic Studies (IMSISS)cs_CZ
thesis.degree.disciplineInternational Master in Security, Intelligence and Strategic Studies (IMSISS)en_US
thesis.degree.programInternational Master in Security, Intelligence and Strategic Studies (IMSISS)cs_CZ
thesis.degree.programInternational Master in Security, Intelligence and Strategic Studies (IMSISS)en_US
uk.thesis.typediplomová prácecs_CZ
uk.taxonomy.organization-csFakulta sociálních věd::Katedra bezpečnostních studiícs_CZ
uk.taxonomy.organization-enFaculty of Social Sciences::Department of Security Studiesen_US
uk.faculty-name.csFakulta sociálních vědcs_CZ
uk.faculty-name.enFaculty of Social Sciencesen_US
uk.faculty-abbr.csFSVcs_CZ
uk.degree-discipline.csInternational Master in Security, Intelligence and Strategic Studies (IMSISS)cs_CZ
uk.degree-discipline.enInternational Master in Security, Intelligence and Strategic Studies (IMSISS)en_US
uk.degree-program.csInternational Master in Security, Intelligence and Strategic Studies (IMSISS)cs_CZ
uk.degree-program.enInternational Master in Security, Intelligence and Strategic Studies (IMSISS)en_US
thesis.grade.csVýborněcs_CZ
thesis.grade.enExcellenten_US
uk.abstract.enArtificial intelligence and machine learning (AI/ML) models are increasingly utilised in every aspect of life and society due to their superhuman abilities to digest large amounts of data and find obscure patterns and correlations. One contentious area of this technological application is in the criminal justice system, where AI/ML is used as a recommendation or decision-making support tool. These applications are particularly popular in the United States of America (USA), the nation with the highest rate of incarceration and correctional budget, to aid in managing overcrowded and overspending facilities. Angwin et al.'s (2016) ground-breaking study found the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) model to be biased against Black defendants and sparked an influential academic debate around algorithmic bias and fairness. This study aims to fill the gap in the scholarship by focusing on the content of COMPAS's recidivism risk assessment questionnaire through a qualitative content analysis within the conceptual framework of Critical Race Theory (CRT). The findings presented in this research are twofold: (1) almost half of the COMPAS questions were opinion-based, thus reducing quantitative neutrality, and (2) there were significant proxy factors for race that...en_US
uk.file-availabilityV
uk.grantorUniverzita Karlova, Fakulta sociálních věd, Katedra bezpečnostních studiícs_CZ
thesis.grade.codeA
uk.publication-placePrahacs_CZ
uk.thesis.defenceStatusO


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