A Systematic Assessment of National Artificial Intelligence Policies: Perspectives from the Nordics and Beyond
Abstract
Echoing the evolving interest and impact of artificial intelligence on society, governments are increasingly looking for ways to strategically position themselves as both innovators and regulators in this new domain. One of the most explicit and accessible ways in which governments outline these plans is through national strategy and policy documents. We follow a systematic search strategy to identify national AI policy documents across twenty-five countries. Through an analysis of these documents, including topic modelling, clustering, and reverse topic-search, we provide an overview of the topics discussed in national AI policies and contrast the differences between countries. Furthermore, we analyse the frequency of eleven ethical principles across our corpus. Our paper outlines implications of the differences between geographical and cultural clusters in relation to the future development of artificial intelligence applications.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Previous work has shown that cultural and geographical differences play a key role in perceptions towards algorithmic systems.
“Previous work has shown that cultural and geographical differences play a key role in perceptions towards algorithmic systems [10,49,97].”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [97]
Cites
PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0850 · Cited by 43,565 · see citing works
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,649 · see citing works
Dimensionalizing Cultures: The Hofstede Model in Context
Hofstede, G. (2011) · Online Readings in Psychology and Culture · doi:10.9707/2307-0919.1014 · Cited by 7,250 · see citing works
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · see citing works
Henrich, J., Heine, S. J., & Norenzayan, A. (2010) · Nature · doi:10.1038/466029a · Cited by 3,441 · see citing works
Correspondence Analysis in Practice
Greenacre, M. J. (2007) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420011234 · Cited by 2,059 · see citing works
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2018) · Nature · doi:10.1038/s41586-018-0637-6 · Cited by 1,859 · cited 5× · see citing works
“Awad et al. also find sub-clusters among the three aforementioned clusters, e.g. Scandinavian countries and Commonwealth countries within the Western cluster [6].”
BackgroundInfluentialOrganisation for Economic Co-Operation and Development
Mercadal, T. (2016) · The SAGE Encyclopedia of Pharmacology and Society · doi:10.4135/9781483349985.n291 · Cited by 1,395 · see citing works
Universal Sentence Encoder for English
Cer, D. M., Yang, Y., Kong, S.-Y., Hua, N., Limtiaco, N., John, R. S., Constant, N., Guajardo-Céspedes, M., Yuan, S., Tar, C., et al. (2018) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d18-2029 · Cited by 1,344 · see citing works
Adams, R. J., Smart, P., & Huff, A. S. (2017) · International Journal of Management Reviews · doi:10.1111/ijmr.12102 · Cited by 995 · see citing works
Searching for grey literature for systematic reviews: challenges and benefits
Mahood, Q., Van Eerd, D., & Irvin, E. (2014) · Research Synthesis Methods · doi:10.1002/jrsm.1106 · Cited by 814 · see citing works
Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174156 · Cited by 743 · see citing works
The pains and gains of microservices: A Systematic grey literature review
Soldani, J., Tamburri, D. A., & van den Heuvel, W. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2018.09.082 · Cited by 438 · see citing works
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000375277 · Cited by 360 · see citing works
Wang, R., Harper, F. M., & Zhu, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376813 · Cited by 211 · see citing works
Grgic-Hlaca, N., Redmiles, E. M., Gummadi, K. P., & Weller, A. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186138 · Cited by 207 · see citing works
A Qualitative Exploration of Perceptions of Algorithmic Fairness
Woodruff, A., Fox, S. E., Rousso-Schindler, S., & Warshaw, J. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174230 · Cited by 190 · see citing works
Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation
Yang, Q., Banović, N., & Zimmerman, J. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173704 · Cited by 130 · see citing works
Towards a Prague Definition of Grey Literature
Schöpfel, J., & GreyNet International, G. L. N. S. (2013) · doi:10.17026/dans-xdx-88ux · Cited by 121 · see citing works
Semantic Multidimensional Scaling for Open-Domain Sentiment Analysis
Cambria, E., Song, Y., Wang, H., & Howard, N. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2012.118 · Cited by 106 · see citing works
Cultural difference and adaptation of communication styles in computer-mediated group brainstorming
Wang, H., Fussell, S. R., & Setlock, L. D. (2009) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/1518701.1518806 · Cited by 99 · see citing works
A segmented topic model based on the two-parameter Poisson-Dirichlet process
Du, L., Buntine, W., & Jin, H. (2010) · Machine Learning · doi:10.1007/s10994-010-5197-4 · Cited by 99 · see citing works
Cultural differences on visual self-presentation through social networking site profile images
Zhao, C. G., & Jiang, G. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979110 · Cited by 77 · see citing works
Smith, C. E., Yu, B., Srivastava, A., Halfaker, A., Terveen, L., & Zhu, H. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376783 · Cited by 77 · see citing works
Crowdsourcing Perceptions of Fair Predictors for Machine Learning
van Berkel, N., Gonçalves, J., Hettiachchi, D., Wijenayake, S., Kelly, R., & Kostakos, V. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359130 · Cited by 73 · see citing works
What is "intelligent" in intelligent user interfaces?
Völkel, S. T., Schneegass, C., Eiband, M., & Buschek, D. (2020) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3377325.3377500 · Cited by 61 · see citing works
Zhao, C. G., HINDS, P. J., & Gao, G. (2012) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2145204.2145219 · Cited by 44 · see citing works
Friedman, B., Höök, K., Gill, B. T., et al. (2008) · doi:10.1145/1463160.1463176 · Cited by 28 · see citing works
Participation Differences in Q&A Sites Across Countries
Oliveira, N., Andrade, N., & Reinecke, K. (2016) · Nordic Conference on Human-Computer Interaction · doi:10.1145/2971485.2971520 · Cited by 23 · see citing works
Interface design for older adults with varying cultural attitudes toward uncertainty
Haddad, S., McGrenere, J., & Jacova, C. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557124 · Cited by 20 · see citing works
Ethics for Artificial Intelligence, Ethics for All
Thomsen, K. (2019) · Paladyn Journal of Behavioral Robotics · doi:10.1515/pjbr-2019-0029 · Cited by 16 · see citing works
Soldani, J. (2019) · ACM SIGSOFT Software Engineering Notes · doi:10.1145/3356773.3356776 · Cited by 12 · see citing works
The Inclusive Images Competition
Atwood, J. R., Halpern, Y., Baljekar, P. N., Breck, E. J., Sculley, D., Ostyakov, P., Nikolenko, S., Ivanov, I., Solovyev, R. A., Wang, W., et al. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-29135-8_6 · Cited by 12 · see citing works
Cloud Computing Security and Privacy: An Empirical Study
Shirazi, F., Seddighi, A., & Iqbal, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58077-7_43 · Cited by 10 · see citing works
The futures of computing and wisdom
Pargman, D. S., Eriksson, E., Comber, R., et al. (2018) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3240167.3240265 · Cited by 9 · see citing works
Crowdsourcing sensitive data using public displays—opportunities, challenges, and considerations
Alorwu, A., van Berkel, N., Gonçalves, J., Oppenlaender, J., López, M. B., Seetharaman, M., & Hosio, S. J. (2020) · Personal and Ubiquitous Computing · doi:10.1007/s00779-020-01375-6 · Cited by 9 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A Decade of Sustainable HCI: Connecting SHCI to the Sustainable Development Goals
Hansson, L. Å. E. J., Cerratto Pargman, T., & Pargman, D. S. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445069 · Cited by 148 · see citing works
Effect of Information Presentation on Fairness Perceptions of Machine Learning Predictors
van Berkel, N., Goncalves, J., Russo, D., Hosio, S., & Skov, M. B. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445365 · Cited by 84 · see citing works
Self-citationYurrita, M., Draws, T., Balayn, A., Murray-Rust, D., Tintarev, N., & Bozzon, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581161 · Cited by 73 · see citing works
- Authors
- van Berkel, N., Papachristos, E., Giachanou, A., Hosio, S., & Skov, M. B.
- Affiliations
- Aalborg University, Denmark
- Aalborg University (DK)
- Universitat Politècnica de València (ES)
- University of Oulu (FI)
- Venue
- Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society
- Publisher
- ACM
- Published
- 2020-10-25
- Pages
- 1-12
- OpenAlex
- W3095727673
- Cited by
- 51
- References
- 36
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 10 · 2023: 9 · 2022: 7 · 2021: 10 · 2020: 1
- FWCI
- 7.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Nordic Conference on Human-Computer Interaction (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Sociology and Political Science › Innovation, Sustainability, Human-Machine Systems
- Keywords
- AI governance, AI regulation, AI ethics, ethical principles, cross-national comparison, Nordic countries
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review