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
322018
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
42019
The usefulness of algorithmic models in policy making
D Kolkman
Government Information Quarterly 37 (3), 2020
12020
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
12018
Complex adaptive systems and the new mobilities paradigm
DA Kolkman
12012
The (in) credibility of algorithmic models to non-experts
D Kolkman
Information, Communication & Society, 1-17, 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
Towards Estimating Happiness using Social Sensing: Perspectives on Organizational Social Network Analysis.
M Atzmueller, D Kolkman, W Liebregts, A Haring
AfCAI, 2018
2018
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
STS and data science: making a data scientist?
D Kolkman
EASST review, 2016
2016
Research Gaps in IA modelling
LS Ayla Alkan, Daan Kolkman, Jaap Rozema
LIAISEoffspring network, 2013
2013
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Articles 1–13