Renee Sieber

McGill University

Professor

1001 Sherbrooke St. W

Montreal, Quebec H3A 1G5

Canada

SCHOLARLY PAPERS

5

DOWNLOADS

454

SSRN CITATIONS

0

CROSSREF CITATIONS

2

Scholarly Papers (5)

1.

Data-Driven Participation: Algorithms, Cities, Citizens, and Corporate Control

Urban Planning (ISSN: 2183-7635) 2016, Volume 1, Issue 2, Pages 101-113
Number of pages: 13 Posted: 01 Nov 2017
Matthew Tenney and Renee Sieber
McGill University - Department of Geography and McGill University
Downloads 238 (177,526)

Abstract:

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big data, civic engagement,data-driven, local government, participation, smart city,urban planning, VGI,volunteered geographic information

2.

The Cost(s) of Open Geospatial Data

Johnson PA, Sieber RE, Scassa T, Stephens M, Robinson PJ. The Cost(s) of Geospatial Open Data. Transactions in GIS. 2017;21:434–445. DOI: 10.1111/tgis.12283
Number of pages: 12 Posted: 24 Jul 2017
University of Waterloo - Department of Geography and Environmental Management, McGill University, University of Ottawa - Common Law Section, Department of Geography and Ryerson University
Downloads 133 (293,097)

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open data, open government, spatial data infrastructures, technology adoption, technology impacts

3.

Comments on the Toronto Police Services Board Proposed Policy on AI Technologies - Montréal Society and Artificial Intelligence Collective (MoSAIC)

Number of pages: 13 Posted: 16 Feb 2022
McGill University, University of Montreal, Mila, McGill University, McGill University, Concordia University, Quebec, McGill University, McGill University, McGill University, McGill University and Toronto Metropolitan University Leadership Lab
Downloads 71 (438,737)

Abstract:

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artificial intelligence, police, explainability, evaluation, procurement, human rights

4.

Civic empowerment in the development and deployment of AI systems

Number of pages: 40 Posted: 11 May 2022
Renee Sieber and Ana Brandusescu
McGill University and McGill University
Downloads 11 (768,095)

Abstract:

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artificial intelligence, civic empowerment, public participation, AI systems

5.

Using Deep Learning and Social Network Analysis to Understand and Manage Extreme Flooding

Journal of Contingencies and Crisis Management, Vol. 28, Issue 3, pp. 251-261, 2020
Number of pages: 11 Posted: 07 Oct 2020
affiliation not provided to SSRN, affiliation not provided to SSRN, McGill University, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 1 (872,692)

Abstract:

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BERT, big data, CrisisNLP, floods, Nebraska, Recurrent Neural Network, supervised classification