Niels van Berkel
Eleftherios Papachristos
Anastasia Giachanou
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.
- Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y. Lim, and Mohan Kankanhalli. 2018. Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems(CHI ’18). Association for Computing Machinery, New York, NY, USA, Article 582, 18 pages. https://doi.org/10.1145/3173574.3174156Google Scholar
Digital Library
- ACM U.S. Public Policy Council and ACM Europe Policy Committee. 2017. Statement on Algorithmic Transparency and Accountability. Commun. ACM (2017).Google Scholar
- Richard J. Adams, Palie Smart, and Anne Sigismund Huff. 2017. Shades of Grey: Guidelines for Working with the Grey Literature in Systematic Reviews for Management and Organizational Studies. International Journal of Management Reviews 19, 4 (2017), 432–454. https://doi.org/10.1111/ijmr.12102Google ScholarCross Ref
- Andy Alorwu, Niels van Berkel, Jorge Goncalves, Jonas Oppenlaender, Miguel Bordallo López, Mahalakshmy Seetharaman, and Simo Hosio. 2020. Crowdsourcing sensitive data using public displays—opportunities, challenges, and considerations. Personal and Ubiquitous Computing(2020). https://doi.org/10.1007/s00779-020-01375-6Google Scholar
- James Atwood, Yoni Halpern, Pallavi Baljekar, Eric Breck, D. Sculley, Pavel Ostyakov, Sergey I. Nikolenko, Igor Ivanov, Roman Solovyev, Weimin Wang, and Miha Skalic. 2020. The Inclusive Images Competition. In The NeurIPS ’18 Competition, Sergio Escalera and Ralf Herbrich (Eds.). Springer International Publishing, Cham, 155–186.Google Scholar
- Edmond Awad, Sohan Dsouza, Richard Kim, Jonathan Schulz, Joseph Henrich, Azim Shariff, Jean-François Bonnefon, and Iyad Rahwan. 2018. The Moral Machine experiment. Nature 563, 7729 (2018), 59. https://doi.org/10.1038/s41586-018-0637-6Google Scholar
- David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993–1022.Google ScholarDigital Library
- E. Cambria, Y. Song, H. Wang, and N. Howard. 2014. Semantic Multidimensional Scaling for Open-Domain Sentiment Analysis. IEEE Intelligent Systems 29, 2 (2014), 44–51. https://doi.org/10.1109/MIS.2012.118Google ScholarCross Ref
- Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, and Ray Kurzweil. 2018. Universal Sentence Encoder for English. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations(EMNLP ’18). 169–174. https://doi.org/10.18653/v1/D18-2029Google ScholarCross Ref
- CIFAR and Brookfield Institute for Innovation + Entrepreneurship. 2019. Rebooting Regulation: Exploring the Future of AI Policy in Canada. https://brookfieldinstitute.ca/report/rebooting-regulation-exploring-the-future-of-ai-policy-in-canada/.Google Scholar
- European Commission. 2020. National Strategies - Knowledge for policy. https://ec.europa.eu/knowledge4policy/ai-watch/national-strategies_en.Google Scholar
- Denmarks Ministry of Finance and Ministry of Industry, Business and Financial Affairs. 2019. National Strategy for Artificial Intelligence. https://en.digst.dk/policy-and-strategy/denmark-s-national-strategy-for-artificial-intelligence/.Google Scholar
- Lan Du, Wray Buntine, and Huidong Jin. 2010. A segmented topic model based on the two-parameter Poisson-Dirichlet process. Machine Learning 81, 1 (2010), 5–19. https://doi.org/10.1007/s10994-010-5197-4Google ScholarDigital Library
- European Commission. 2018. Communication from the European Commission – Artificial Intelligence for Europe no 2018/137. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2018:237:FIN.Google Scholar
- European Commission. 2020. On Artificial Intelligence - A European approach to excellence and trust. Technical Report. 27 pages. https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_enGoogle Scholar
- Finland’s Ministry of Economic Affairs and Employment. 2019. Leading the way into the era of artificial intelligence : Final report of Finland’s Artificial Intelligence Programme 2019. http://urn.fi/URN:ISBN:978-952-327-437-2.Google Scholar
- Organisation for Economic Co-operationand Development. Year unknown. AI initiatives worldwide. http://www.oecd.org/going-digital/ai/initiatives-worldwide/.Google Scholar
- Foundation for Science and Technology. 2019. AI Portugal 2030. https://www.incode2030.gov.pt/en/ai-portugal-2030.Google Scholar
- Batya Friedman, Kristina Hook, Brian Gill, Lina Eidmar, Catherine Sallmander Prien, and Rachel Severson. 2008. Personlig Integritet: A Comparative Study of Perceptions of Privacy in Public Places in Sweden and the United States. In Proceedings of the 5th Nordic Conference on Human-Computer Interaction: Building Bridges(NordiCHI ’08). 142–151. https://doi.org/10.1145/1463160.1463176Google ScholarDigital Library
- Michael Greenacre. 2017. Correspondence analysis in practice. CRC press.Google Scholar
- Nina Grgić-Hlača, Elissa M. Redmiles, Krishna P. Gummadi, and Adrian Weller. 2018. Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction. In Proceedings of the 2018 World Wide Web Conference(Lyon, France) (WWW ‘18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 903–912. https://doi.org/10.1145/3178876.3186138Google ScholarDigital Library
- Shathel Haddad, Joanna McGrenere, and Claudia Jacova. 2014. Interface Design for Older Adults with Varying Cultural Attitudes toward Uncertainty. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’14). Association for Computing Machinery, New York, NY, USA, 1913–1922. https://doi.org/10.1145/2556288.2557124Google ScholarDigital Library
- Joseph Henrich, Steven J Heine, and Ara Norenzayan. 2010. Most people are not WEIRD. Nature 466, 7302 (2010), 29–29.Google ScholarCross Ref
- Geert Hofstede. 2011. Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture 2, 1 (2011), 8. https://doi.org/10.9707/2307-0919.1014Google ScholarCross Ref
- Anna Jobin, Marcello Ienca, and Effy Vayena. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1, 9 (2019), 389–399. https://doi.org/10.1038/s42256-019-0088-2Google ScholarCross Ref
- Peter Lange, Ulrik Boe Kjeldsen, Maja Tofteng, Anja Krag, and Kasper Lindgaard. 2014. The coexistence of two Ecolabels: The Nordic Ecolabel and the EU Ecolabel in the Nordic Countries. Nordic Council of Ministers.Google Scholar
- Lithuanian Ministry of Economy and Innovation. 2019. Lithuanian Artificial Intelligence Strategy: A vision of the future. http://kurklt.lt/wp-content/uploads/2018/09/StrategyIndesignpdf.pdf.Google Scholar
- Quenby Mahood, Dwayne Van Eerd, and Emma Irvin. 2013. Searching for grey literature for systematic reviews: challenges and benefits. Research Synthesis Methods 5, 3 (2013), 221–234. https://doi.org/10.1002/jrsm.1106Google ScholarCross Ref
- Nordic Council of Ministers for Digitalisation. 2018. AI in the Nordic-Baltic region. https://www.norden.org/en/declaration/ai-nordic-baltic-region.Google Scholar
- Norwegian Ministry of Local Government and Modernisation. 2019. National Strategy for Artificial Intelligence. https://www.regjeringen.no/en/dokumenter/nasjonal-strategi-for-kunstig-intelligens/id2685594.Google Scholar
- Future of Life Institute. 2020. National and International AI Strategies. https://futureoflife.org/national-international-ai-strategies/.Google Scholar
- Publications Office of the European Union. 2020. Concept scheme - 7206 Europe. https://op.europa.eu/s/n3ru.Google Scholar
- Nigini Oliveira, Nazareno Andrade, and Katharina Reinecke. 2016. Participation Differences in Q&A Sites Across Countries: Opportunities for Cultural Adaptation. In Proceedings of the 9th Nordic Conference on Human-Computer Interaction(NordiCHI ’16). 10. https://doi.org/10.1145/2971485.2971520Google ScholarDigital Library
- Organisation for Economic Co-operation and Development. 2019. Recommendation of the Council on Artificial Intelligence – OECD/Legal/0449. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449.Google Scholar
- Daniel Pargman, Elina Eriksson, Rob Comber, Ben Kirman, and Oliver Bates. 2018. The Futures of Computing and Wisdom. In Proceedings of the 10th Nordic Conference on Human-Computer Interaction(NordiCHI ’18). Association for Computing Machinery, 960–963. https://doi.org/10.1145/3240167.3240265Google ScholarDigital Library
- Joachim Schöpfel. 2010. Towards a Prague definition of grey literature. In Twelfth International Conference on Grey Literature. 11–26.Google Scholar
- Farid Shirazi, Adnan Seddighi, and Amna Iqbal. 2017. Cloud Computing Security and Privacy: An Empirical Study. In Human-Computer Interaction. Interaction Contexts, Masaaki Kurosu (Ed.). Springer International Publishing, 534–549.Google Scholar
- C. Estelle Smith, Bowen Yu, Anjali Srivastava, Aaron Halfaker, Loren Terveen, and Haiyi Zhu. 2020. Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems(CHI ’20). 1–14. https://doi.org/10.1145/3313831.3376783Google ScholarDigital Library
- Jacopo Soldani. 2019. Grey Literature: A Safe Bridge Between Academy and Industry?SIGSOFT Softw. Eng. Notes 44, 3 (Nov. 2019), 11–12. https://doi.org/10.1145/3356773.3356776Google ScholarDigital Library
- Jacopo Soldani, Damian Andrew Tamburri, and Willem-Jan Van Den Heuvel. 2018. The pains and gains of microservices: A Systematic grey literature review. Journal of Systems and Software 146 (2018), 215 – 232. https://doi.org/10.1016/j.jss.2018.09.082Google ScholarCross Ref
- The United States Government. 2020. Artificial Intelligence for the American People. https://www.whitehouse.gov/ai/ai-american-values/.Google Scholar
- A. C. Tricco, E. Lillie, W. Zarin, K. K. O’Brien, H. Colquhoun, D. Levac, D. Moher, M. D. J. Peters, T. Horsley, L. Weeks, S. Hempel, E. A. Akl, C. Chang, J. McGowan, L. Stewart, L. Hartling, A. Aldcroft, M. G. Wilson, C. Garritty, S. Lewin, C. M. Godfrey, M. T. Macdonald, E. V. Langlois, K. Soares-Weiser, J. Moriarty, T. Clifford, Ö. Tunçalp, and S. E. Straus. 2018. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 169, 7 (10 2018), 467–473.Google Scholar
- USA’s Select Committee on Artificial Intelligence of the National Science & Technology Council. 2019. The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update. https://www.whitehouse.gov/ai/ai-american-innovation/.Google Scholar
- Niels van Berkel, Jorge Goncalves, Danula Hettiachchi, Senuri Wijenayake, Ryan M. Kelly, and Vassilis Kostakos. 2019. Crowdsourcing Perceptions of Fair Predictors for Machine Learning: A Recidivism Case Study. Proceedings of the ACM on Human-Computer Interaction - CSCW 3 (2019), 28:1–28:21. https://doi.org/10.1145/3359130Google ScholarDigital Library
- Cédric Villani, Yann Bonnet, Bertrand Rondepierre, 2018. For a Meaningful Artificial Intelligence: Towards a French and European Strategy.Google Scholar
- Sarah Theres Völkel, Christina Schneegass, Malin Eiband, and Daniel Buschek. 2020. What is ‘Intelligent’ in Intelligent User Interfaces? A Meta-Analysis of 25 Years of IUI. In Proceedings of the 25th International Conference on Intelligent User Interfaces(IUI ’20). 477–487. https://doi.org/10.1145/3377325.3377500Google ScholarDigital Library
- Hao-Chuan Wang, Susan F. Fussell, and Leslie D. Setlock. 2009. Cultural Difference and Adaptation of Communication Styles in Computer-Mediated Group Brainstorming. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI ’09). Association for Computing Machinery, New York, NY, USA, 669–678. https://doi.org/10.1145/1518701.1518806Google ScholarDigital Library
- Ruotong Wang, F. Maxwell Harper, and Haiyi Zhu. 2020. Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems(CHI ’20). 1–14. https://doi.org/10.1145/3313831.3376813Google ScholarDigital Library
- Allison Woodruff, Sarah E. Fox, Steven Rousso-Schindler, and Jeffrey Warshaw. 2018. A Qualitative Exploration of Perceptions of Algorithmic Fairness. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, Article 656, 14 pages. https://doi.org/10.1145/3173574.3174230Google ScholarDigital Library
- Mike Woolridge, Peter Millican, and Paula Boddington. 2020. Ethics for Artificial Intelligence. https://www.cs.ox.ac.uk/efai/resources/alphabetical-list-of-resources/.Google Scholar
- Qian Yang, Nikola Banovic, and John Zimmerman. 2018. Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, Article 130, 11 pages. https://doi.org/10.1145/3173574.3173704Google ScholarDigital Library
- Chen Zhao, Pamela Hinds, and Ge Gao. 2012. How and to Whom People Share: The Role of Culture in Self-Disclosure in Online Communities. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work(CSCW ’12). Association for Computing Machinery, New York, NY, USA, 67–76. https://doi.org/10.1145/2145204.2145219Google ScholarDigital Library
- Chen Zhao and Gonglue Jiang. 2011. Cultural Differences on Visual Self-Presentation through Social Networking Site Profile Images. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’11). Association for Computing Machinery, New York, NY, USA, 1129–1132. https://doi.org/10.1145/1978942.1979110Google ScholarDigital Library
Index Terms
- A Systematic Assessment of National Artificial Intelligence Policies: Perspectives from the Nordics and Beyond
Index terms have been assigned to the content through auto-classification.
Recommendations
The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation
AbstractIn July 2017, China’s State Council released the country’s strategy for developing artificial intelligence (AI), entitled ‘New Generation Artificial Intelligence Development Plan’ (新一代人工智能发展规划). This strategy outlined China’s aims to become the ...
Towards ethical aspects on artificial intelligence
AIKED'09: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data basesThis paper presents the role of ethics in developing artificial intelligence, and how the artificial intelligence could change our perspective, because artificial intelligence in fact is all around us. Artificial intelligence is an important part of our ...
Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference
AbstractArtificial intelligence (AI) receives attention in media as well as in academe and business. In media coverage and reporting, AI is predominantly described in contrasted terms, either as the ultimate solution to all human problems or the ultimate ...
Comments