Transparent to whom? No algorithmic accountability without a critical audience J Kemper, D Kolkman Information, Communication & Society 14 (1), 2081-2096, 2018 | 227 | 2018 |
The usefulness of algorithmic models in policy making D Kolkman Government Information Quarterly 37 (3), 2020 | 49 | 2020 |
How to build models for government: Criteria driving model acceptance in policymaking DA Kolkman, P Campo, T Balke-Visser, N Gilbert Policy Sciences 49, 489-504, 2016 | 36 | 2016 |
The (in) credibility of algorithmic models to non-experts D Kolkman Information, Communication & Society 25 (1), 93-109, 2022 | 32 | 2022 |
Is firm growth random? A machine learning perspective A van Witteloostuijn, D Kolkman Journal of Business Venturing Insights 11 (1), 1-5, 2019 | 28 | 2019 |
F** k the algorithm?: what the world can learn from the UK’s A-level grading fiasco D Kolkman Impact of Social Sciences Blog, 2020 | 13 | 2020 |
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 | 10 | 2018 |
Challenges in Data Science Projects with SMEs: An Analysis and Decision Support Tool D Kolkman, R Sneep Available at SSRN 3343092, 2019 | 2 | 2019 |
Is public accountability possible in algorithmic policymaking? The case for a public watchdog D Kolkman Impact of Social Sciences Blog, 2020 | 1 | 2020 |
Glitch Studies and the Ambiguous Objectivity of Algorithms D Kolkman, J Kemper SSRN, 2017 | 1 | 2017 |
Complex adaptive systems and the new mobilities paradigm DA Kolkman | 1 | 2012 |
The Elementary Scenario Component Metric for Summarization Evaluation M Kirilov, D Kolkman, BJ Butijn Proceedings of the 5th International Conference on Natural Language and …, 2022 | | 2022 |
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 |