Daan Kolkman
Title
Cited by
Cited by
Year
Transparent to whom? No algorithmic accountability without a critical audience
J Kemper, D Kolkman
Information, Communication & Society 14 (1), 2081-2096, 2018
402018
How to build models for government: criteria driving model acceptance in policymaking
DA Kolkman, P Campo, T Balke-Visser, N Gilbert
Policy Sciences 49 (4), 489-504, 2016
122016
Is firm growth random? A machine learning perspective
A van Witteloostuijn, D Kolkman
Journal of Business Venturing Insights 11 (1), 1-5, 2019
52019
The usefulness of algorithmic models in policy making
D Kolkman
Government Information Quarterly 37 (3), 2020
22020
Data Science in Strategy Machine learning and text analysis in the study of firm growth
D Kolkman, A van Witteloostuijn
Tinbergen Institute Discussion Paper, 2018
22018
The (in) credibility of algorithmic models to non-experts
D Kolkman
Information, Communication & Society, 1-17, 2020
12020
Complex adaptive systems and the new mobilities paradigm
DA Kolkman
12012
Is public accountability possible in algorithmic policymaking? The case for a public watchdog
D Kolkman
Impact of Social Sciences Blog, 2020
2020
Challenges in Data Science Projects with SMEs: An Analysis and Decision Support Tool
D Kolkman, R Sneep
Available at SSRN 3343092, 2019
2019
Glitch Studies and the Ambiguous Objectivity of Algorithms
D Kolkman, J Kemper
SSRN, 2017
2017
Models in policy making
DA Kolkman
University of Surrey, 2016
2016
Research Gaps in IA modelling
LS Ayla Alkan, Daan Kolkman, Jaap Rozema
LIAISEoffspring network, 2013
2013
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Articles 1–12