Løvstakken, Bergen, Norway photo by @sharon_christina

Nordic Artificial Intelligence

Building on Nordic AI Strategies to Develop Responsible AI

I decided to write a mockup today of what a course on AI in Society could look like, and I thought it would be wise to combine a clear focus in the Nordic with perspectives on artificial intelligence. When there is a legacy of thinking in regards to equality and welfare, then there must be considerations taken related to this with the deployment of new technology. An addition to the discussion of AI must be the recognition of the need for urgent ways to address the local and global impact of the climate crisis.

The text that follows is a mockup of a course module and should only be taken as a sketch.

Location of the Nordic countries — geographical and cultural region in Northern Europe and the North Atlantic, where they are most commonly known as Norden (literally “the North”)

Nordic Artificial Intelligence

Course content

This course will give you an understanding of Nordic strategies on Artificial Intelligence (AI) and questions relating to implementing AI in welfare, health, gender equality, income inequality and the environment. As such this course will be focused on AI policy from the Nordic countries compared with other AI policies around the world. The aim is to understand how the Nordic societies are using AI as an extension of these guidelines.

The growth of artificial intelligence has of recent times been rapid and wide reaching. Both private company challenges and governance issues are being tackled assisted by machine learning techniques. Within this development a range of questions are being raised in regards to the fairness of AI.

The decisions that are implemented have made us come to term with historic decision patterns or recognise our failure to take societal concerns into the equation. Welfare, health, equality and the climate crisis are issues that needs to be addressed. This course invites you to do so through public engagement as well as collaboration between technologists and social thinkers.

1. History of artificial intelligence, the Nordic collaboration and AI strategies.
2. Public discussions on the topics AI in welfare, labour and the climate crisis.
3. Theories, methods and approaches to AI in society. Visiting AI projects.
4. Designing applications of AI with technologists and social thinkers.

Learning outcome

Knowledge

• An overview of the history of artificial intelligence with a brief outline of the global development and its development in the nordic countries.
• Outline of the Nordic countries as geographical and cultural region. The focus will bring an overview of all the Nordic AI strategies with a comparison against other national or regional strategies.
• Current challenges in development of artificial intelligence relating to labour, welfare and health. With examples of how machine learning is being implemented in different Nordic countries.
• Understanding how AI can adversely or negatively impact the current climate crisis and the main standpoints within the Nordic region relating to this topic.

Skills

• Ability to synthesise and critique a variety of positions relating to AI strategies and policies.
• Writing rudimentary code in Python.
• Applications of transdisciplinary methods to collaborate on the development of solutions in the field of AI.
• Examining and addressing government tenders relating to AI.

General competence

• Enhanced understanding of artificial intelligence as a field and concept.
• Enhanced teamwork capability, in particular collaborating with programmers or technologists in discussing societal impact of different solutions.
• Awareness of the shift from human-centred to planet-centred AI.

Curriculum

Books:
Callon, M. (2009). Acting in an uncertain world. MIT press.

O’neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.

Articles:

Bauer, Susanne (2014). From Administrative Infrastructure to Biomedical Resource: Danish Population Registries, the ‘Scandinavian Laboratory’, and the ‘Epidemiologist’s Dream’. Science in Context 27(2), 187- 213

Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. The Cambridge handbook of artificial intelligence, 316, 334.

Crootof, R. (2014). The killer robots are here: legal and policy implications. Cardozo L. Rev., 36, 1837. Link.

Delgado, A., Rommetveit, K., Barceló, M., & Lemkow, L. (2012). Imagining high-tech bodies: Science fiction and the ethics of enhancement. Science Communication, 34(2), 200–240.

Gray, J., Gerlitz, C., & Bounegru, L. (2018) Data infrastructure literacy. Big Data & Society.

Green, B. Data Science as Political Action. Link: https://scholar.harvard.edu/files/bgreen/files/data_science_as_political_action.pdf

Gunning, D. (2017). Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web, 2. Link: https://www.darpa.mil/attachments/XAIProgramUpdate.pdf

Irving, G., & Askell, A. (2019). AI Safety Needs Social Scientists. Distill, 4(2), e14.

Leonelli, Sabine, Brian Rappert, Gail Davies: Data Shadows: Knowledge, Openness, and Absence. Science, Technology, & Human Values 42 (2), 191–202 (2017).

Lutz, C. (2019). Digital inequalities in the age of artificial intelligence and big data. Human Behavior and Emerging Technologies, 1(2), 141–148.

Nilsson, N. J. (2005). Human-level artificial intelligence? Be serious!. AI magazine, 26(4), 68–68.

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., … & Luccioni, A. (2019). Tackling Climate Change with Machine Learning. arXiv preprint arXiv:1906.05433.

Ruppert, Evelyn, John Law, Mike Savage, Reassembling the Social Science Methods: The Challenge of Digital Devices in Theory Culture and Society, 30 (4)pp. 22–46 (2013)

Ziewitz, Malte: Governing Algorithms: Myth, Mess, and Methods. Science, Technology, & Human Values 41 (1) 3–16 8 (2016).

Strategies and policies:

Datatilsynet (2015) The Great Data Race. How commercial utilisation of personal data challenges privacy. Report, November 2015. https://www.datatilsynet.no/globalassets/global/english/engelsk-kommersialisering-endelig.pdf

Danish National Strategy for AI (2019): https://investindk.com/insights/the-danish-government-presents-national-ai-strategy

European approach to Artificial Intelligence and Robotics. Link: https://ec.europa.eu/digital-single-market/en/artificial-intelligence#A-European-approach-to-Artificial-Intelligence

Finland’s Age of Artificial Intelligence Turning Finland into a leading country in the application of artificial intelligence Objective and recommendations for measures (2017) link: http://julkaisut.valtioneuvosto.fi/bitstream/handle/10024/160391/TEMrap_47_2017_verkkojulkaisu.pdf?sequence=1&isAllowed=y

Norway’s AI Strategy (TBA) — not released yet, but will be soon 2019–2020.

Norway research ethics (currently Norwegian TBA in English) link: https://www.etikkom.no/globalassets/documents/publikasjoner-som-pdf/forskningsetisk-betenkning-om-kunstig-intelligens.pdf

Sweden — National approach to artificial intelligence https://www.government.se/491fa7/contentassets/fe2ba005fb49433587574c513a837fac/national-approach-to-artificial-intelligence.pdf

Rousku, K., Andersson, C., Stenfors, S., Lähteenmäki, I., Limnéll, J., Mäkinen, K., … & Rissanen, O. P. (2019). Glimpses of the future. Data policy, artificial intelligence and robotisation as enablers of wellbeing and economic success in Finland. Link: http://julkaisut.valtioneuvosto.fi/handle/10024/161675

This is #500daysofAI and you are reading article 166. I write one new article about or related to artificial intelligence every day for 500 days.