VALCRI: Difference between revisions
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=== Data visualization === |
=== Data visualization === |
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VALCRI can present data and information in visual formats such as maps, [[timeline]]s, dispersion diagrams, and process charts.<ref name=":1">{{Cite journal| |
VALCRI can present data and information in visual formats such as maps, [[timeline]]s, dispersion diagrams, and process charts.<ref name=":1">{{Cite journal|last1=Anslow|first1=Craig|last2=Rooney|first2=Chris|last3=Kodagoda|first3=Neesha|last4=Wong|first4=William|date=2015|title=Police Analyst Workstation|url=http://dx.doi.org/10.1145/2817721.2823498|journal=Proceedings of the 2015 International Conference on Interactive Tabletops & Surfaces - ITS '15|pages=307–311 |location=New York, New York, USA|publisher=ACM Press|doi=10.1145/2817721.2823498|isbn=978-1-4503-3899-8|s2cid=2002596 }}</ref> This creates an analyst [[Dashboard (business)|dashboard]] that is designed to be integrated within the workforce and allow for investigative reasoning based on database information.<ref name=":52"/> The [[Visualization (graphics)|visualizations]] are interactive and encourage cooperative input from human analysis. |
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VALCRI also employs [[algorithm]]s such as PCA, MDS, and [[T-distributed stochastic neighbor embedding|t-SNE]] to embed data points into graphical representations.<ref>{{Cite journal|last=Wolfgang|first=Jentner|date=2018|title=Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool|journal=The Visual Computer|volume=34|pages=1225–1241}}</ref> This feature allows for the statistical and mathematical calculation of similarity and correlation levels between different crime data sets through different algorithmic models which each have their own strengths and weaknesses.<ref>{{Cite web|last=Sacha|first=Dominik|date=February 2017|title=Applying Visual Interactive Dimensionality Reduction to Criminal Intelligence Analysis|url=https://bib.dbvis.de/uploadedFiles/VALCRIWP2017011InteractiveVisualDimensionReduction.pdf}}</ref> |
VALCRI also employs [[algorithm]]s such as PCA, MDS, and [[T-distributed stochastic neighbor embedding|t-SNE]] to embed data points into graphical representations.<ref>{{Cite journal|last=Wolfgang|first=Jentner|date=2018|title=Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool|journal=The Visual Computer|volume=34|issue=9 |pages=1225–1241|doi=10.1007/s00371-018-1483-0 |s2cid=3342538 |url=https://eprints.mdx.ac.uk/23484/1/main.pdf }}</ref> This feature allows for the statistical and mathematical calculation of similarity and correlation levels between different crime data sets through different algorithmic models which each have their own strengths and weaknesses.<ref>{{Cite web|last=Sacha|first=Dominik|date=February 2017|title=Applying Visual Interactive Dimensionality Reduction to Criminal Intelligence Analysis|url=https://bib.dbvis.de/uploadedFiles/VALCRIWP2017011InteractiveVisualDimensionReduction.pdf}}</ref> |
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== Concerns == |
== Concerns == |
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=== Legal and ethical challenges === |
=== Legal and ethical challenges === |
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During the development of VALCRI, an Independent Ethics Board (IEB) and Security, Privacy, and Legal Group (SEPL) was created to monitor potential ethical challenges and roadblocks that the project would introduce.<ref name=":7">{{Cite |
During the development of VALCRI, an Independent Ethics Board (IEB) and Security, Privacy, and Legal Group (SEPL) was created to monitor potential ethical challenges and roadblocks that the project would introduce.<ref name=":7">{{Cite book|last=Duquenoy|first=P.|title=Societal Implications of Community-Oriented Policing and Technology |date=2018|chapter=Addressing Ethical Challenges of Creating New Technology for Criminal Investigation: The VALCRI Project|series=SpringerBriefs in Criminology |pages=31–38 |doi=10.1007/978-3-319-89297-9_4 |isbn=978-3-319-89296-2 |s2cid=169570412 |chapter-url=https://link.springer.com/chapter/10.1007/978-3-319-89297-9_4}}</ref> With the specialists in these boards, there were numerous concerns that were identified in respect to potential ethics and legality issues.<ref name=":7" /> |
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One issue that was identified by these boards was potential complications with human [[privacy]].<ref name=":0">{{Citation| |
One issue that was identified by these boards was potential complications with human [[privacy]].<ref name=":0">{{Citation|last1=Marquenie|first1=Thomas|title=Operationalization of Information Security through Compliance with Directive 2016/680 in Law Enforcement Technology and Practice|date=2019-10-01|url=http://dx.doi.org/10.1017/9781780688909.005|work=Security and Law|pages=97–128|publisher=Intersentia|isbn=978-1-78068-890-9|access-date=2020-11-03|last2=Quezada|first2=Katherine|doi=10.1017/9781780688909.005 |s2cid=213698899 }}</ref> With the advent of a comprehensive database system that would be able to share billions of different data points for law enforcement, VALCRI faces potential roadblocks in navigating through different regional policies and laws regarding data privacy and security.<ref name=":0" /> This potential issue has been addressed in VALCRI by creating a dedicated group supervising [[data management]] policy in the software.<ref>{{Cite web|last=Marquenie|first=Thomas|date=2017|title=Data analytics in a police context–addressing legal issues in VALCRI|url=https://lirias.kuleuven.be/1711892?limo=0}}</ref> |
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=== Cognitive and sense-making bias === |
=== Cognitive and sense-making bias === |
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VALCRI's data analysis capabilities offer criminal analysts a wider set of data points to base their conclusions on. [[Vienna University of Technology]]'s research based on 120 case studies introduces the risk of cognitive and sense-making [[bias]] playing a significant role in influencing the conclusions that law enforcement agents draw, based on the visualizations provided by VALCRI.<ref name=":6">{{Cite |
VALCRI's data analysis capabilities offer criminal analysts a wider set of data points to base their conclusions on. [[Vienna University of Technology]]'s research based on 120 case studies introduces the risk of cognitive and sense-making [[bias]] playing a significant role in influencing the conclusions that law enforcement agents draw, based on the visualizations provided by VALCRI.<ref name=":6">{{Cite book|last=Pohl|first=Margit|title=2014 IEEE Joint Intelligence and Security Informatics Conference |date=2014|chapter=Sensemaking and Cognitive Bias Mitigation in Visual Analytics|page=323 |doi=10.1109/JISIC.2014.68 |isbn=978-1-4799-6364-5 |s2cid=1758913 |chapter-url=https://ieeexplore.ieee.org/document/6975606}}</ref> These risks can be mitigated in VALCRI by redesigning machine learning models and implementing de-biasing mechanisms such as Klein's data frame model so that it becomes easier to identify [[cognitive bias]] and adjust analysis objectives based on the findings.<ref name=":6" /> |
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==See also== |
==See also== |
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Revision as of 18:55, 13 July 2022
 | The topic of this article may not meet Wikipedia's general notability guideline. (July 2018) |
Visual Analytics for Sense-making in Criminal Intelligence Analysis (VALCRI) is a software tool that helps investigators to find related or relevant information in several criminal databases.[1][2] The software uses big data processes to aggregate information from a wide array of different sources and formats and compiles it into visual and readable arrangements for users.[3] It is used by various law enforcement agencies and aims to allow officials to utilize statistical information in their operations and strategy.[4][3] The project is funded by the European Commission and is led by Professor William Wong at Middlesex University.[5]
Features
Automatic search
VALCRI can automatically search numerous databases using dedicated search engines. Previously, investigators would need to employ an average of 73 SQL queries and wait up to three days to find the right cases.[6] The tool utilizes machine learning mechanisms to screen through masses of unstructured data to identify similarities between cases, and performs associative searching to comes up with reports based on the search criteria of users.[7]
Data visualization
VALCRI can present data and information in visual formats such as maps, timelines, dispersion diagrams, and process charts.[5] This creates an analyst dashboard that is designed to be integrated within the workforce and allow for investigative reasoning based on database information.[7] The visualizations are interactive and encourage cooperative input from human analysis.
VALCRI also employs algorithms such as PCA, MDS, and t-SNE to embed data points into graphical representations.[8] This feature allows for the statistical and mathematical calculation of similarity and correlation levels between different crime data sets through different algorithmic models which each have their own strengths and weaknesses.[9]
Concerns
Legal and ethical challenges
During the development of VALCRI, an Independent Ethics Board (IEB) and Security, Privacy, and Legal Group (SEPL) was created to monitor potential ethical challenges and roadblocks that the project would introduce.[10] With the specialists in these boards, there were numerous concerns that were identified in respect to potential ethics and legality issues.[10]
One issue that was identified by these boards was potential complications with human privacy.[11] With the advent of a comprehensive database system that would be able to share billions of different data points for law enforcement, VALCRI faces potential roadblocks in navigating through different regional policies and laws regarding data privacy and security.[11] This potential issue has been addressed in VALCRI by creating a dedicated group supervising data management policy in the software.[12]
Cognitive and sense-making bias
VALCRI's data analysis capabilities offer criminal analysts a wider set of data points to base their conclusions on. Vienna University of Technology's research based on 120 case studies introduces the risk of cognitive and sense-making bias playing a significant role in influencing the conclusions that law enforcement agents draw, based on the visualizations provided by VALCRI.[13] These risks can be mitigated in VALCRI by redesigning machine learning models and implementing de-biasing mechanisms such as Klein's data frame model so that it becomes easier to identify cognitive bias and adjust analysis objectives based on the findings.[13]
See also
References
- ^ AI detective analyses police data to learn how to crack cases[full citation needed]
- ^ Visual Analytics for Sense-making in CRiminal Intelligence analysis[full citation needed]
- ^ a b Schlehahn, Eva (2017). "The Operationalisation of Transparency in VALCRI" (PDF).
- ^ Improvements to machine-intelligence will improve accuracy and speed up police investigations
- ^ a b Anslow, Craig; Rooney, Chris; Kodagoda, Neesha; Wong, William (2015). "Police Analyst Workstation". Proceedings of the 2015 International Conference on Interactive Tabletops & Surfaces - ITS '15. New York, New York, USA: ACM Press: 307–311. doi:10.1145/2817721.2823498. ISBN 978-1-4503-3899-8. S2CID 2002596.
- ^ Visual analytics for brighter criminal intelligence[full citation needed]
- ^ a b "Final Report Summary - VALCRI (Visual Analytics for Sense-making in CRiminal Intelligence analysis)". 2018.
- ^ Wolfgang, Jentner (2018). "Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool" (PDF). The Visual Computer. 34 (9): 1225–1241. doi:10.1007/s00371-018-1483-0. S2CID 3342538.
- ^ Sacha, Dominik (February 2017). "Applying Visual Interactive Dimensionality Reduction to Criminal Intelligence Analysis" (PDF).
- ^ a b Duquenoy, P. (2018). "Addressing Ethical Challenges of Creating New Technology for Criminal Investigation: The VALCRI Project". Societal Implications of Community-Oriented Policing and Technology. SpringerBriefs in Criminology. pp. 31–38. doi:10.1007/978-3-319-89297-9_4. ISBN 978-3-319-89296-2. S2CID 169570412.
- ^ a b Marquenie, Thomas; Quezada, Katherine (2019-10-01), "Operationalization of Information Security through Compliance with Directive 2016/680 in Law Enforcement Technology and Practice", Security and Law, Intersentia, pp. 97–128, doi:10.1017/9781780688909.005, ISBN 978-1-78068-890-9, S2CID 213698899, retrieved 2020-11-03
- ^ Marquenie, Thomas (2017). "Data analytics in a police context–addressing legal issues in VALCRI".
- ^ a b Pohl, Margit (2014). "Sensemaking and Cognitive Bias Mitigation in Visual Analytics". 2014 IEEE Joint Intelligence and Security Informatics Conference. p. 323. doi:10.1109/JISIC.2014.68. ISBN 978-1-4799-6364-5. S2CID 1758913.