Shaping the future

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Shaping the future

A comparative case study of Finnish and Norwegian approaches to political strategy of

artificial intelligence

Eirin M. Evjen

Master’s Thesis

TIK Centre for Technology, Innovation and Culture Faculty of Social sciences

UNIVERSITY OF OSLO

Fall 2018

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Shaping the future:

A comparative case study of Finnish and Norwegian

approaches to political strategy of artificial intelligence

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Shaping the future: A comparative case study of Finnish and Norwegian approaches to political strategy of artificial intelligence

Eirin M. Evjen

Faculty of Social Sciences, University of Oslo http://www.duo.uio.no/

Trykk: Reprosentralen, Universitetet i Oslo

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Abstract

The public discourse on artificial intelligence (AI) is rapidly increasing, in tandem with faster- paced development in the field of artificial intelligence. With these advancements, multiple short- and long-term issues and considerations are appearing. The longer-term consequences are linked to the development of artificial general intelligence (AGI). Several countries are now outlining strategies for how to approach artificial intelligence and deliberating which policy measures should be enforced. Visions of the future and imagined realities are embedded in these strategies. These current frameworks will have an impact on and direct the course of how policy-makers understand and handle AGI in the future. how it should be are embedded in these strategies, and therefore have an impact on how policy-makers handle AGI in the future.

Through a comparative case study of Finnish and Norwegian proposed national strategies for AI, this thesis aims to understand how Finnish and Norwegian policy-makers perceive and approach artificial general intelligence and long-term consequences of the technology. What are the implications of their portrayal, visions, and perceived risks of AGI, and what does this say about how to best approach AGI and its potentially significant consequences?

To answer this question, the two proposed strategies are analysed, in addition to nine qualitative interviews and review of various publications and legislature. The data is analysed through an abductive approach, using iterative cycles of moving back and forth between the empirical data and theories in social and science studies and futures studies. This process resulted in five main concepts: not only focusing on utilisation, but also development of technology, being mindful of words and phrases used to describe technology and its risks, being humble when dealing with complex and uncertain risks, and including social scientists and future generations in the deliberation and development processes.

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Acknowledgements

I am truly grateful for being able to not only write about something so exciting and meaningful, but to do it with the support of so many. There are many to thank for helping me through this educational, exciting, and challenging process. First and foremost, thank you to my two supervisors, Marjolein van Asselt and Tina Talleraas: To Marjolein for reminding me to aim high, and Tina for keeping me on the ground. Thank you to Eirik and Maria B. for keeping me focused, to Marte and Maria N. for keeping me sane, to Vegard for guidance in all things technical, and to Jørgen and Anna for tirelessly supporting me. Thank you to my fellow class-mates for all the fun and insightful conversations. Thank you to Sindre for somehow always being by my side through the ups and downs, of which there have been many. Lastly, thank you to Oda Helene – for being the best sister and proof-reader a master’s student could have.

October 2018, Oslo

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1 Introduction

In January 2015, the UN received an open letter, to date signed by over 8000 people, that called for a significant increase in research on how to ensure that artificial intelligence (AI) systems are robust and beneficial, meaning that the technology makes decisions to the benefit of society (Future of Life, 2015). The letter came with a document outlining research priorities as a suggestive guide to existing and future research on this topic (Russel, Dewey, &

Tegmark, 2015). The authors concluded these priorities by stating that:

… success in the quest for artificial intelligence has the potential to bring

unprecedented benefits to humanity, and it is therefore worthwhile to research how to maximise these benefits while avoiding potential pitfalls (Russel, Dewey, & Tegmark, 2015, p. 112).

There is no clear consensus of how to define artificial intelligence (Luger, 2009; Russell &

Norvig, 2016). The definitions usually include some variation of a machine’s, system’s, or computer programme’s ability to learn and/or solve problems based on different types of learning and computing mechanisms (Russell & Norvig, 2016; Tørresen, 2013). The definitions are sometime very strict, suggesting that there are no AI programs or systems today. Others are more open, even defining a calculator as an artificial intelligence.

Nonetheless, there is a consensus that, however far the development of artificial intelligence has come, there are no computer programmes today that are as intelligent as humans in all aspects (Bostrom & Yudkowsky, 2014; Wang & Goertzel, 2007). Such programmes are said to have artificial general intelligence (AGI), meaning that they are not only good at a concrete task, but have general capabilities and therefore can be applied generally.

On the one hand, there are potentially several tremendous benefits of a technology with similar or higher level of intelligence as humans. Such a technology can be used to solve important problems that human beings have yet to solve. On the other hand, there are multiple experts who have pointed at potentially devastating negative outcomes of developing AGI.

Some of the potential negative consequences of AGI are linked to the difficulties in aligning the technology’s goals with our values and ambitions. That is to say, in pursuit of a goal, it may unintentionally inflict harm or simply not recognize the negative consequences of its actions. Two reasons for why it is difficult to align the AGI’s goals with ours are: first,

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formalising what human values are is nontrivial, and second, specifying these values in the programming language that the system is written in is easier said than done.

Müller and Bostrom (2016) conducted a survey of about 550 experts and their views on the likelihood of high-level machine intelligence, meaning something close to AGI, being developed within a certain time-frame, what the risks of such a development are, and how fast these will develop. Keep in mind that this survey does not necessarily say when an AGI will be developed, but what experts in the field deem to be the most probable given certain parameters. The results of the survey were that the experts perceive there to be a one-in-two chance that a high-level machine intelligence will be developed around 2040-2050, and a nine-in-ten chance of such an intelligence being developed by 2075. Further, the experts estimate that this will likely result in superintelligence, a machine with intelligence that far surpasses human intelligence, thirty years after such high-level machine intelligence is developed. They then predict that the likelihood of this development being ‘bad’ or

‘extremely bad’ for humanity is a one-in-three chance.

There is a high number of uncertainties when it comes to the development of artificial general intelligence and its repercussions. Keeping this in mind, based on these experts’ opinions, and the concern shared by the over 8000 signatories to the open letter to the UN, the development of such intelligence seems worthy of further exploration and assessment. As the potential risks are so large and there are so many uncertainties surrounding the trajectory of the development, advancements in the research on AGI and how to ensure the beneficial outcomes and reduction of the risks are very valuable.

What makes this topic even more pressing to investigate is that the way countries and policy- makers approach and handle artificially intelligent systems is currently in the making. The European Union and many countries are producing strategies for how to deal with artificial intelligence as an emerging technology (AI Singapore, n.d.; Canadian Institute for Advanced Research, n.d.; Digital Single Market, 2018; National Institution for Transforming India, 2018). Due to the formative nature of this stage in policy-making concerning AI and AGI, it is crucial to pay attention to how governments are addressing these technologies and their risks.

How policy-makers perceive and handle technologies and their risks are frequent subjects of analysis in science and technology studies (STS) and futures studies. Both are interdisciplinary fields that are concerned with the production and implementation of science

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3 and technology. In STS, the focus is on how the development and utilisation of science and technology relates to and is coproduced by society (Jasanoff, 2004; Sismondo, 2010). In futures studies, systemic risks and benefits of potential and probable scenarios for the future are examined to guide further action (Schultz, 2015). When analysing policy responses to emerging technologies with large future risks, STS and futures studies are complementary.

Concepts in STS can explain ways in which technology and society affect each other and how visions of the future affect policy today. Futures studies provide insight into how to go about assessing and addressing complex risks.

In the literature, visions of the future and perceptions of technology have been analysed to highlight how policy-makers approach emerging technologies. For instance, through the sociology of expectations, Brown and Michael (2003) argue that how agents in the past have imagined the future to be shapes how present decision-makers view the future, and what they deem to be feasible policy measures. Through sociotechnical imaginaries, Jasanoff and Kim (2015) outline how desired and undesired views of the future shape how nations handle technology today. Further, concepts such as risk governance and technologies of humility provide guides of how to best approach systemic and complex risks (Jasanoff, 2003; Van Asselt & Renn, 2011).

In order to understand more about how artificial general intelligence and its risks are perceived and dealt with, and now this informs policy, I am comparing two proposed national strategies in Finland and Norway. Both countries are in the starting phase of developing policies and plans for dealing with artificial intelligence. By comparing the two cases, the peculiarities of each case are elucidated. Moreover, a comparative case study can also produce more generic insights about how to best approach AGI and its potential risks.

This study’s research question is therefore: How do Finnish and Norwegian policy-makers i) perceive and approach artificial general intelligence and long-term

consequences of the technology,

ii) how does this affect which policy-measures are deemed feasible and desirable, and

iii) what does this tell us about how to best approach AGI and its potentially significant and long-term consequences?

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To answer the research question, I have analysed the Steering Group for the Finnish AI Programme’s (SGAIP) (2017) interim report and the Norwegian Board of Technology’s (2018) report on artificial intelligence. In addition, I have conducted nine qualitative interviews with various actors in each country and reviewed relevant publications and legislature. Through an abductive approach, going back and forth between the empirical data and theory, I have compiled five main concepts from STS and futures studies that can explain the empirical findings:

i) Focus on development processes and not only the implementation of technology;

ii) The formulation and phrasing of visions and their impact;

iii) The importance of humility when approaching complex and uncertain risks without treating the technology and the risks as inevitable;

iv) Inclusion of social scientists in the deliberations and implementation phases of new technologies;

v) And ensuring the representation of various groups of stakeholders in the processes.

The analysis of the data based on these five concepts show that there are both key differences and similarities between the two cases. In addition, the two cases can tell us something about how AGI and its risks can be addressed. Firstly, a focus on the implementation of technology rather than the development of it can lead to a dismissal of AGI and thereby its risks. Further, the words and phrases used to describe AGI and the long-term consequences can justify the postponement or neglect of addressing and looking earnestly at the technology. It is meaningful to be humble in acknowledging the complexities and uncertainties regarding AGI, while also not letting these complexities lead to inaction. While devoting efforts to more domain specific AI and its consequences on society is paramount, one should be careful of using such efforts as a reason not to address AGI and the more long-term and abstract risks.

Moreover, one should make sure that social scientists are included in the deliberation process, and that the research of social scientists and other disciplines inform the development of the technology. Lastly, in order to make sure that the development of AGI leads to the most beneficial outcome, it is essential to include all relevant groups of stakeholders in the development process. Particularly, the interests of future generations should be represented, as they are affected by the choices being made today.

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5 This study provides three recommendations for addressing AGI and its risks:

I. Address artificial general intelligence, though not at the expense of domain specific AI.

II. Ensure the representation of future generations in the development and deliberation processes.

III. Address the risks of artificial general intelligence through an appropriate framework that takes complexity and uncertainty into consideration.

The structure of the thesis is as follows. First, the theoretical background of the analysis is outlined and discussed, the five main concepts are presented, and the study’s contribution to the literature is reviewed. The purpose of this is to ground the empirical study in theory and to make explicit where the analysis fits into the existing literature. Next, the methodology used in the analysis is mapped out. This chapter gives a description of the data collection process and depicts the abductive process of data analysis that produced the findings of the study.

Further, the quality of the study, in terms of reliability, validity and generalisability, is reviewed.

The findings are presented in a three-fold structure. Firstly, there is an overview of efforts to address AI in Finland and Norway. Secondly, the two cases are compared on how AI and AGI are defined, how risks of AGI are perceived, to what extent social scientists and different groups of stakeholders are included in the deliberation and development process, and, finally, what type of policies and political action the two governments view as appropriate and feasible when it comes to AI. Thirdly, the differences and similarities are reviewed based on the five main concepts from STS and futures studies.

In the penultimate chapter, the third part of the research question is answered, namely what the findings from the two cases tell us about how to best approach AGI and its potentially significant and long-term consequences. Furthermore, three recommendations for addressing the development of AGI and its risks. Lastly, the implications of the findings for the literature are then discussed, and suggestions for future research are presented. In the conclusion, the analysis, findings, and discussions are summarised.

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2 Science, technology, and futures studies

Science and technology studies is an interdisciplinary field in which the object of observation is the development and implementation of science and technology in relation to society. It is a highly empirical field, researching various types of sciences and technologies, the processes that constitute them and how they affect and are affected by society. STS also provides an arena to study the different considerations and issues surrounding policy-making regarding technology and science.

In this thesis, I have chosen to elaborate on five main concepts in STS and relate it to futures studies in order to discuss how policy-makers can and should address artificial general intelligence and its potential consequences. Futures studies examine potential and probable scenarios for the future through an interdisciplinary approach building on political, social, technological, and economic sciences (Schultz, 2015; Slaughter, 1998). It is a holistic approach, looking at systemic facets and issues (Schultz, 2015). Furthermore, futures studies try to uncover the assumptions behind visions of the future (Schultz, 2015; Slaughter, 1998).

As the methodology chapter will reveal, choosing these five concepts from STS and futures studies is the result of an abductive approach, an iterative process going back and forth between data and theory.

The main concepts are:

i) Focus on development processes and not only the implementation of technology;

ii) The formulation and phrasing of visions and their impact;

iii) The importance of humility when approaching complex and uncertain risks without treating the technology and the risks as inevitable;

iv) Inclusion of social scientists in the deliberations and implementation phases of new technologies;

v) And ensuring the representation of various groups of stakeholders in the processes.

Before elaborating on these five main concepts, I will first give an overview of studies of artificial intelligence in STS.

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2.1 STS, artificial intelligence, and risk

In the field of STS, artificial intelligence has been studied and used as a case for understanding knowledge production. Hubert Dreyfus (1992), for example, argues that AI is limited, because it is based solely on formalised facts and information gathered by experts. As there are many fields and aspects that are difficult, if not impossible, to formalise, the usefulness of AI is constrained. Harry Collins (1990) takes this further by arguing that AI only works well in those areas where humans have chosen to work with a machine-like behaviour. Such behaviour occurs in areas like calculating and optimising (Collins 1990).

Later, Collins and Kusch (1998) proposed that it is impossible for an AI to take over tasks that cannot be completed by copying previous completions of that task or similar tasks.

STS scholars have looked at both the application of and development and research of AI. For example, Diana E. Forsythe’s (1993) studied the construction of work in artificial intelligence. Joel Novek (2002) analysed the implementation of an automated drug distribution system. On the other hand, Nathan Ensmenger (2012) studied the emergence of chess in AI research and its significance to the development of AI, and Landström, Whatmore and Lane (2013) looked at the process of learning through computer model improvisations.

It appears that artificial general intelligence, the form of AI that is as intelligent as humans in all aspects, has not been much discussed in STS, though technologies of a similar nature have been studied. The development of nanotechnology and adhering social and ethical issues, for example, have been explored in studies such as Cynthia Selin (2007), Elena Simakova (2012), and Valerie L. Hanson (2011).

It is not just studies of AI that are relevant in this thesis, but also studies of the effects of risk and approaches to assessments of risk. Ulrich Beck (1992) and Anthony Giddens (1990) discuss what they call ‘risk society,’ “a systematic way of dealing with hazards and insecurities induced and introduced by modernisation itself” (Beck, 1992, p.21). According to Beck (1992) and Giddens (1990), society’s increased focus on future and safety generates an impression of risk, coupled with the fast pace of scientific and technological advancements.

Two approaches to handling complex risks characterised by uncertainty and broadness in scope are outlined in the discussion of the main concepts. These are technologies of humility (Jasanoff, 2003) and risk governance (Van Asselt & Renn, 2011). What follows is an

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elaboration of the five main concepts of STS relating to dealing with AI and risk that inform this study.

2.2 Main concepts

2.2.1 Development versus application

The field of science and technology studies is a response to the deterministic view of technology that emerged in the 20^th century: developments in science and technology simply occurs, and people must follow. The description of the 1933 World’s fair in Chicago where the US’ latest achievements in technology and science were showcased illustrates this well:

“Science discovers, genius invents, industry applies, and man adapts himself to, or is moulded by, new things.” Contrary to this passive approach, STS is interested in increasing our understanding and ability to actively influence the social processes of developing and implementing new technologies. This can be called a moderately social constructivist approach that stands in opposition to technological determinism.

Technological determinism consists of two crucial parts (MacKenzie & Wajcman, 1999;

Wyatt, 2003). Firstly, any developments in technology or science occurs independently and outside society. Technology is therefore not affected by social, economic or political changes.

The second part is that developments in technology and science themselves are exogenous factors that affect society and social change. With this deterministic view, policy responses to emerging technologies are merely reactions to the development of technology and are in fact caused or determined by technology rather than the other way around (Downs, 1972;

Kingdon, 2014).

In STS, these views are challenged. Pinch and Bijker (1984) outlined the theory of social construction of technology (SCOT) and argued that the end product in the development process of a technology is the result of various social factors and interpretative flexibility.

This means that different features of the technology are interpreted differently by different groups, and that who is developing the technology and who the technology is intended for shapes the outcome of the technology itself (Pinch & Bijker, 1984).

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9 This view was criticised for being to socially deterministic, and that technology indeed does affect society (Akrich, 1992; Latour, 1987; Winner, 1980). It was argued that technology and society produce and affect each other, and that it is therefore important to study the development process of technology as well as the implementation and application of the technology (MacKenzie & Wajcman, 1999; Jasanoff, 2004).

This implies that policy-makers need to be aware of both the decision-making processes involved and the utilisation of technology. Those who are affected by the technology should therefore be represented in the process in order to make sure that the implementation of the technology is of benefit to both users and non-users (Oudshoorn & Pinch, 2003; Wyatt, 2003;

Wynne, 1992). Consequently, it is insufficient to only look at the utilisation of a technology and conversely only the development process – a combination of these two perspectives are needed.

2.2.2 Power of words

Researchers such as Nik Brown and Mike Michael have looked at how actors in the past viewed and envisioned the future shapes the expectations and visions of the future that exist today (Brown & Michael, 2003). The interdisciplinary field called the sociology of expectations draws on STS, as well as innovation studies, economics, sociology, and history and philosophy of science (Borup, Brown, Konrad, & Van Lente, 2006). Other notable STS- works in this field are Guice (1999) and van Lente and Rip (1998).

As Borup et.al (2006) explains, a key concept in the sociology of expectations is that how the expectations are phrased and embedded in text shapes the feasibility of different actions and the desirability of different outcomes:

After a time, or even rather quickly, expectations may be seen to exhibit certain material and social path dependencies (lock-in or irreversibility) becoming the basis for future envisioning, a predisciplining of the imagination through the legacy of former expectations (Borup et.al, 2006, p.293).

The expectations that policy-makers have for the future are shaping and limiting the way they view certain issues and what deem as appropriate policy measures. Furthermore, such visions shape how policy-makers imagine the future to be, and therefore their imagination of society.

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Moreover, formulations and how certain aspects are emphasised in public reports create a precedence that exude to future policy-makers and their perceived window of opportunities.

As Urry (2016) and Wajcman (2017) argue, future visions have huge impacts on society.

They embed what is important for the society and what the common good is (Urry, 2016;

Wajcman, 2017). As a consequence of expectations shaping policy-makers, how a technology and its prospective capacities are perceived will be influencing society both today and tomorrow. It is therefore important to be mindful and deliberate our choices of words and images when discussing the future of a technology.

2.2.3 Humility and simplicity

In her article on Technologies of humility, Sheila Jasanoff (2003) argues that researchers and decision-makers need to find ways to adjust and relate to the partiality and uncertainties of science and technology. Jasanoff (2003, p.238) claims that the government and policy-makers use predictive, and often simple, methods to try to reassure the public and keep developing the technology. Policy-makers need certainty to take action, and when there is a lack of certainty, they create it through these often predictive methods (Jasanoff, 2003).

She explains it further: “Well-defined, short-term risks command more attention than indeterminate, long-term ones, especially in cultures given to technological optimism”

(p.239). Through simplistic predictive methods, what falls outside the scope of the method is often dismissed while what falls in is often overemphasised (Jasanoff, 2003, p.239).

Furthermore, these methods frame what is possible and feasible. Aspects of the technology that are too messy or difficult to handle are disregarded as irrelevant (Jasanoff, 2003, p.239).

Following this sentiment, one should be aware of not presenting the considerations and issues surrounding the technology as simple and certain.

Many of the concerns that are raised in technologies of humility are addressed in the field of risk governance. Defined by Renn, Klinke & van Asselt (2011), risk governance “denotes both the institutional structure and the policy process that guide and restrain collective activities of a group, society or international community to regulate, reduce or control risk problems” (p.231). This approach is influenced by risk studies in sociology and psychology, STS, political science and law studies (van Asselt & Renn, 2011). The aim of risk governance is to provide a framework with which one can deal with different types of risk.

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11 Similar to technologies of humility, risk governance elucidates the characteristics of the risks themselves. In risk governance, they call risks that cannot be dealt with easily ‘systemic’.

According to van Asselt & Renn (2011), systemic “describes the extent to which a risk is embedded in the larger contexts of societal processes” (p.436). Such risks increase in intensity, and are often complex, ambiguous and/or surrounded by uncertainty (Klinke &

Renn, 2002; Renn, 2008; Van Asselt & Renn, 2011). Treating systemic risks as simple means addressing them inadequately, as the interdependencies as well as ripple and spill over effects are dismissed (Van Asselt & Renn, 2011).

At the same time, it is important to be aware of the consequences of painting something as vast and catastrophic. As John Urry (2016, p.52) shows, there are some dangers to catastrophism. Framing the future is partly performative, it has the potential to be a self- fulfilling prophecy (Urry, 2016, p.52). It is therefore essential to be aware of the framing of uncertainties and threats linked to the technology.

In her study of the role of envisioning ethical nanotechnology, Valerie L. Hanson (2011) suggests that visions of the future frame what is deemed as relevant social and ethical issues.

Whatever falls outside of the visions is put on hold to be looked at some other time.

Meanwhile, the implementation and development of technology should keep its course (Hanson, 2011). This postponing effect is explained as follows:

The effects of the deployment of these visions are to emphasise postponement of SEI (social and ethical issues) research, frame a vague role for SEI dependent on

technological development and contribute to a vision of a vaguely defined, yet ethical future nanotechnology that helps halt concerns about nanotechnology while also directing attention away from present concerns and possible action (p.1).

In this way, such vague ethical visions of the future are in fact delaying the critical evaluation of current social and ethical issues at stake.

2.2.4 A call for social scientists

Technology and science simultaneously affect and are constituted by society. A natural extension of the main arguments made in STS is that social sciences have an indisputable important role in the policy- and decision-making processes concerning strategies of technological development (Sismondo, 2010; Skjølsvold, 2015). MacKenzie and Wajcman

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(1999) addressed the social shaping of technology (SST). Instead of focusing on the consequences of technology, studies of SST look at the content of technology, meaning the development process (MacKenzie & Wajcman, 1999; Williams & Edge, 1996).

In her book States of Knowledge, Sheila Jasanoff (2004) also looks at how the development process of science and technology is influenced by society. The development of technology and the trajectory of society are therefore not two separate entities unfolding in a vacuum.

Rather, they evolve together, “embedded in social practices, identities, norms, conventions, discourses, instruments and institutions - in short, in all the building blocks of what we term the social” (Jasanoff, 2004, p.3). With what is called ‘co-production’, Jasanoff (2004) claims that society, scientific knowledge, and technological artifacts influence each other.

Following the theory of co-production, Sheila Jasanoff and Sang-Hyun Kim (2015) looked at how a shared vision of the role of technology form what they call ‘sociotechnical imaginaries.

They define these imaginaries as “collectively held, institutionally stabilized, and publicly performed visions of desirable futures, animated by shared understandings of forms of social life and social order attainable through, and supportive of, advances in science and technology” (Jasanoff, p.4). Sociotechnical imaginaries thereby include both the desired and undesired futures that the technology can produce, as well as who are defined as the different relevant agents in making these futures a reality.

John Urry (2016) argues in his book What is the future? that social sciences also need to be more present in future planning. The future is inherently social, and it is therefore important that social scientists look at who is defining the future and how it is being planned (Law &

Urry, 2004; Urry, 2016). Urry (2016) suggests that social sciences need to take back planning and develop new types of planning for the future that are less focused on linear approaches, but that are multidimensional and take into account the complexities of the interwoven relationship between technology and society. Furthermore, social scientists need to be wary of how to design models for future planning as envisioning the future is a performative process and can lead to a self-fulfilling prophecy.

Not only is technology social – so is the future, and how the future is envisioned shapes how policy-makers act today. Social sciences are therefore essential to strategies of technological development and should be included in the deliberation process. The perspective of social

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2.2.5 Representation of future generations

In science and technology studies, incorporating the voices of those who are affected by the technology is a central theme. This entails both those who are users and inflicted non-users of the technology (Oudshoorn & Pinch, 2003; Wyatt, 2003). Oudshoorn and Pinch (2003) argue that the users of technologies are often underestimated in the co-production of the technology.

They do this by looking at how different approaches in STS such as SCOT, feminism, semiotic and culture and media studies incorporate the role of the users and non-users. Wyatt (2003) argues that non-users are often underrepresented in deliberations and decision-making processes, and that they should get more focus in STS. She promotes different types of users:

resisters who are actively opposing, rejectors who have stopped using the technology, those who are socially or technically excluded and those who are expelled from using the technology due to factors such as cost (Wyatt, 2003).

Also other stakeholders such as NGOs, industry, governmental institutions and citizens in general are relevant to include in the deliberation processes. Furthermore, there should be more public participation in the decision-making processes (Sismondo, 2010). As Sismondo (2010) writes:

Scientific knowledge is the result of the mobilisation of resources to produce

agreement among key researchers. Similarly, successful technologies are the result of the interplay among multiple actors and materials to produce artifacts that can be said to serve specific interests. Knowledge and artifacts may reflect the socialisation and training of actors who make them and may also reflect assumptions that are more widely held. Thus, if there is a problem of democracy, it is a problem of the way in which science and technology are socially constructed, or a problem of the parts of society that participate in the constructing. (p.186)

It is therefore a salient concept in STS that citizens and different stakeholders should be included in the deliberation processes in order to make the technology as beneficial to the most number of people and groups as possible. Some ways of doing this are to have open discussions with the public, to include citizen opinions in the starting phases, and to facilitate

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hybrid forums between experts, technologists, politicians, and citizens involved (Callon, Lascoumes, & Barthe, 2009; Latour, 2004; Law, 2008).

In technologies of humility, Jasanoff (2003) the vulnerability to risk of different groups needs to be assessed. According to Jasanoff (2003) it is important to recognise socio-economic factors, and assess people as individuals rather than populations (p.241). Further, Jasanoff (2003) argues that “through participation in the analysis of their vulnerability, ordinary citizens may regain their status as active subjects, rather than remain undifferentiated objects in yet another expert discourse” (p.241). Therefore, it is through being allowed to partake in the process of defining once own capabilities and vulnerabilities that citizens can be part of the deliberation process.

Representation of different stakeholders is also important in risk governance. In this framework, communication and inclusion is one of three principles, alongside integration and reflection. By communication and inclusion, van Asselt and Renn (2011) refer to “exchanges between policy-makers, experts, stakeholders and the general public, and among themselves”

(p.440). Furthermore, it means including various actors and ensuring that they play significant roles in the framing or pre-assessing of the risk (International Risk Governance Conference [IRGC], 2005; Renn, 2008; Van Asselt & Renn, 2011). Van Asselt and Renn (2011, p.441) justify the focus on inclusion of various actors by stating that diversified inputs from different sources increases the capability to address risks. In addition, they argue that in sync with democratic principles, those who are affected by the risks or how the risks are dealt with should have the right to participate (van Asselt & Renn, 2011). Lastly, van Asselt and Renn (2011) conclude that an inclusive and diverse approach increases the social robustness of the outcome.

Although less represented in STS literature, future generations are also recognised as key stakeholders that need to be included. Neither technologies of humility nor risk governance explicitly mention the incorporation of future generations as relevant voices. However, both frameworks open up for it and do not explicitly dismiss them as stakeholders. The representation of future generations is a salient topic in futures studies, especially when it comes to studying climate change. A few noteworthy mentions are Edith Brown Weiss Weiss (1990) on sustainable development and Edward A. Page (2006) on climate change and justice.

Some have looked at existential risks in general, such as Richard A. Slaughter (1998), Bruce E. Tonn (2009) and Natalie Jones, Mark O’Brien and Thomas Ryan (2018). There is also

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15 William R. Kramer (2011) on the topic of colonising Mars, and the case of including other than just the future generation of homo sapiens, but this thesis is focusing on humanity.

2.3 Contribution to the literature

The analysis of data in this study is based on an iterative and cyclical approach, alternating between the empirical data and theory. This abductive approach has resulted in the combination of concepts in STS and futures studies. STS allows one to study how science and technology relates to society, and what is important to keep in mind when developing and implementing a technology. While STS theories such as sociotechnical imaginaries have emphasised visions of the future, these are mainly concerned with using desired and undesired futures to explain the contemporary situation. Futures studies, on the other hand, are more concerned with how the policies and measures put in place today affect the future and our ability to handle different scenarios.

The result of the abductive approach has been the five key concepts presented in this chapter.

Through the consolidation of STS and futures studies, this study has shed light on the construction of policy responses and the role images of futures play, or seem to play, in the process. Through the comparative analysis of Finland and Norway, this study has also found more general considerations to be made when approaching artificial general intelligence and its risks.

This study’s contribution to the literature is therefore both conceptual and empirical. The five concepts are an effort to find a framework with which one can elucidate the construction and implications of policy responses to complex emerging technologies with large potential consequences. Seemingly, this is one of the first studies of artificial intelligence policy comparing Finland and Norway. Further, looking at policy-making and future visions of artificial general intelligence adds to the literature on studies of AI in STS, as the object of observation in policy studies have mainly been more domain specific definitions of AI.

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3 Methodology

The aim of this study is to understand more about how policy-makers respond to emerging technologies. To do this, I have chosen to compare Finland and Norway by looking at the existing proposed strategies for artificial intelligence in each country. Additionally, I have interviewed different types of policy-makers and others to understand more about which perceptions lie behind the different strategies and the approaches taken by each country. The main empirical data for the thesis consists of the Steering Group of the AI programme in Finland’s interim report and the Norwegian Board of Technology’s report. Additional empirical sources are nine qualitative interviews, the Norwegian Data Protection Authority’s report, a few informants, published articles and presentations, and relevant legislature in the two countries.

This thesis is a comparative case study using qualitative data and an abductive analysis approach. In this chapter, the methodology of the study is outlined. I start by explaining why I have chosen to do a comparative case study of Finland and Norway. Subsequently, I summarise how the data was collected, and provide a run-down of the different documents that have been reviewed and how the qualitative interviews were conducted, and with whom.

I then sketch out the data analysis process and what it entails to use an abductive approach.

Lastly, I evaluate the reliability, validity, and generalisability of the study, in addition to discussing the ethical considerations that have been made.

3.1 Comparative case study

3.1.1 On case studies

In order to get more insight into how governments deal with an emerging technology such as artificial intelligence, I chose to employ the method of comparative case study with two cases:

Finland and Norway. The purpose of using comparative studies is to situate and elucidate each case, as well as finding best practices and attempting to export them to other cases (Jasanoff, 2005). When examining visions of the future and how an emerging technology is perceived, there are implicit assumptions and beliefs that lie behind the visions and perceptions (Jasanoff, 2015). By contrasting two nation-states, these assumptions and beliefs

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17 are brought into light and scrutinised (Jasanoff, 2015). By comparing two or more cases, each case gets clearer.

Furthermore, there are likely things each case can learn from each other, and other cases can learn from them. Finding these practices and teaching them, however, is a complex issue. This is because the interplay between technology and politics is embedded in the culture, history and society, which is often specific to a nation-state (Jasanoff, 2005; Jasanoff, 2015). Not only does this mean that transferring knowledge into another context is difficult, it can also mean that disregarding the cultural specificities can lead to unsuccessful implementations of the practice (Jasanoff, 2005). This is discussed further when addressing the generalisability of this study.

3.1.2 Choosing Finland and Norway

As more and more technology entrepreneurs, scientists and philosophers have raised their concerns with regards to potential long-term consequences of AI, I became increasingly interested in how governments deal with these concerns, if at all. Artificial intelligence seems to be perceived by some as a transformative technology - a technology that can drastically change our society. How governments drive and influence the development and how policy- makers react to the progression of this technology, seems very important because the impact of these processes is so potentially large. Artificial intelligence is not the only technology in this category; biotechnology is another example of a transformative technology.

In 2017, the Global Priorities Project (GPP) published a report on existential risks, commissioned by the Finnish Ministry for Foreign Affairs (Farquhar et al., 2017). The development of artificial general intelligence was mentioned as one of these existential risks in the report. I knew about GPPs report beforehand and found that the Finnish government were in the midst of developing a strategy for the implementation of artificial intelligence. In this way, Finland is an interesting case of a country where the policies and measures regarding artificial intelligence is very much in the making.

Norway is interesting to compare with Finland as the two countries are fairly similar when it comes to socio-economic factors. They are both industrialised well-fare states, with relatively similar democratic values and principles. They are therefore good candidates for comparison.

Reviewing what is being done in Finland informed me when looking at Norway, and vice

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versa. In addition, I easily gained access to information and informants in both countries, and in Norway in particular.

3.2 Data collection

The data selection is mainly made up of two reports, the interim report from the Steering Group of the Artificial Intelligence Programme and the recent report from the Norwegian Board of Technology on artificial intelligence. These are supplemented by nine semi- structured interviews, a few additional informants, published articles, the Norwegian Data Protection Authority’s report, and relevant legislature in Finland and Norway. In this section, I justify the use of document analysis and qualitative interviews, and explain what the different methods entail.

3.2.1 Public documents and reports

Public documents are often seen as clear and unanimous statements, representing the government as a whole. However, behind every document and report, there is a story of choices being made, negotiations between individuals, and a context to be aware of (Rapley, 2011; Sismondo, 2010). In this analysis, governmental reports and other relevant documents can tell us something about how policy-makers define artificial intelligence and how they view the technology’s role in the society. Such documents are an integral part of policy studies in STS.

In this thesis I am mainly analysing the two strategies proposed by the Finnish Steering Group and Norwegian Board of Technology. In addition, I am triangulating the findings from the strategies with other documents and qualitative interviews. Triangulation is the act of using multiple approaches to test the same research question and comparing the results of each approach (Heale & Forbes, 2013; Patton, 1999). This is to gain more insight into the scope and content of various statements and phrases made in the two reports, and to uncover some of the underlying assumptions behind the report.

The types of documents I am analysing in this study are government publications, official reports, interim reports, announcements, and websites. I chose to limit the scope and range of documents to what can be described as quite recent documents, meaning from the past two to

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19 five years. My chosen topic, policy responses to AI as an emerging technology, is a very contemporary subject and thus requires looking at rather recent documents. However, artificial intelligence is not a new technology and it might have been relevant to look at older documents as well. Nonetheless, it is mainly in recent years that governments have begun to address this topic, and there are therefore a limited number of public documents available from anything before this period.

Furthermore, as it is becoming increasingly salient, more and more documents are published on the topic of AI. It is important to be flexible when studying such a vibrant topic. In the last few months, for instance, there have been multiple initiatives in the research community as discussed in the analysis. The Board of Technology’s report is an example of such a document that was published rather recently. I chose to use the report as a main part of my data selection as it included a concrete strategy for artificial intelligence in Norway. However, due to time constraint I did not get the chance to interview someone from the Board of Technology, but was able to ask the project manager of the report a few follow-up questions.

Again, the main empirical data for this analysis is the Steering Group’s interim report and the Board of Technology’s report on AI (SGAIP, 2017; Board of Technology, 2018). The additional documents that were reviewed were:

• The Norwegian Data Protection Authority’s report on artificial intelligence (Data Protection Authority [Datatilsynet], 2018),

• The Norwegian Research Council’s programme plan for research funding on ICT and digital innovation (Research Council [Forskningsrådet], 2018),

• The Finnish Ministry for Foreign Affairs’ futures review released June 2018 (Ministry for Foreign Affairs, 2018),

• The Global Priorities Project report on global existential risks, commissioned by the Unit for Policy Planning and Research under the Finnish Ministry for Foreign Affairs (Farquhar et.al, 2017).

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3.2.2 Qualitative interviews

To supplement the documents, I have conducted nine semi-structured interviews with various actors in Norway and Finland. The aim of such qualitative interviews is to both cover pre- determined topics, as well as being open to tangents and new topics. It is therefore important to have a semi-structured interview guide and sufficient time to allow for deeper responses and relevant digressions (Tjora, 2012, p. 129). As Kvale (1997) argues, qualitative interviews tell us something about the world seen from the interviewee’s point of view. This method allows me to get insight into how the decision-makers view the future of their country and what they emphasise and prioritise.

Moreover, the type of data a researcher is after has direct implications for the structure and form of the interview (Thagaard, 2013). The relationship between the researcher and interviewee can become very asymmetric if the interview has a rigid structure (Thagaard, 2013). This is suitable in some instances, but as there is little knowledge of the topic beforehand and I am interested in the views and perceptions of the interviewees, not necessarily the facts, a less structured interview provides me with the leeway and atmosphere needed to get the information I want (Tjora, 2012, p.111). A more structured interview in the form of a survey would make it possible to interview more people and get more data points but would come at the expense of depth and the possibility that new topics and insights emerge from my inquiry.

Interviewees

The nine interviews were with people from different sectors and were found using the snowballing method. Additionally, I tried to interview similar type of actors in each case. I had five Norwegian interviews and four Finnish ones. There were five interviews in Norway and four in Finland. In Norway, I interviewed two representatives from the Data Protection Authority, a researcher and the vice-president at Centre for Artificial Intelligence Research (CAIR), the state secretary of the Ministry of Local Affairs and Modernisation (KMD), the programme coordinator of a funding programme at the Research Council, and a researcher at the Norwegian Institute of International Affairs (NUPI).

The purpose of the interview with the two representatives from the Norwegian Data Protection Authority was to gain more insight into their report on artificial intelligence. The

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21 interview with Morten Goodwin, the researcher from CAIR, provided the perspective from the research sector. The objective of the interview with Paul Chaffey, the state secretary from the KMD, was to better understand the government’s point of view. Additionally, the KMD is tasked with outlining ethical guidelines for the use of artificial intelligence in Norway. The Norwegian Research Council has a funding programme called IKTPLUSS where research projects in information and communications technology (ICT) and digital innovation. Some of the projects funded are researching artificial intelligence. I therefore interviewed André Fossen Mlonyeni, the programme coordinator of IKTPLUSS. Lastly, I interviewed Erik Reichborn-Kjennerud, a researcher working on a project on the future of emerging technologies such as artificial intelligence in warfare at NUPI.

In the Finnish case, I interviewed a representative from AI Finland, a senior researcher at the Unit for Policy Planning and Research under the Finnish Ministry for Foreign affairs, the Finnish Data Protection Ombudsman, and a researcher at Aalto University. I interviewed the representative from AI Finland in order to get more insight to the Steering Group’s report and activities. Ossi Piironen, the senior researcher at the Unit for Policy Planning and Research was part of the follow-up process of the report on global risks by the Global Priorities Project.

The Data Protection Ombudsman, Reijo Aarnio, was interviewed to get the same perspective as the Data Protection Authority in the Norwegian case. The researcher from Aalto University, Matti Nelimarkka, is linked to the Finnish Centre for AI (FCAI), a research institution similar to CAIR in Norway.

In the beginning of the research and writing process, I planned on basing the analysis of the Norwegian case to a larger extent on interviews as there, at the time, was no comparable document to the Finnish Steering Group’s interim report outlining a strategy for AI in Norway. However, such a document was published at a later point in the writing process, and I therefore decided to focus the analysis on the Board of Technology’s report in addition to the Steering Group’s interim report. This is the reason why there are more Norwegian interviewees, and why I have not interviewed anyone from the Board of Technology.

Interview guide

In semi-structured interviews, it is important to have an interview guide that provides the structure for talking about key topics as well as new insights (Tjora, 2012). The guide used in

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the interviews were structured in five main sections: warm-up, influences of policy, desired futures, undesired futures, and round-up. The first and last section were always in that order, but the three sections in the middle sometimes had a different order depending on the flow of the conversation. The full interview guide can be found in the appendix.

The purpose of the warm-up section was to get the conversation started and set the tone for the interview. The interviewees were asked about their backgrounds and the program or institution they were part of. Additionally, I asked them to define AI and what they perceived the capacity of AI to be. This creates both a useful baseline for understanding the rest of the information that was provided throughout the interview as well as being a point of interest in its own right.

The second section was about what the interviewees themselves believe influences AI policy.

This was to gain more insight into AI in each country, but also gauge the perceived importance of different groups and institutions. In this section, both what the interviewees mentioned and not mentioned proved to be of interest. The third and fourth sections were on the interviewees desired and undesired futures when it comes to their country in general and artificial intelligence in particular. The purpose of these sections was to get a sense of the perceived role of artificial intelligence in realising desired futures and which future risks the actors identify.

In the round-up section, I asked about differences between Norway and Finland in their approaches to AI, and whom else they recommend I should talk to concerning this topic. This was firstly to get more insight into the actors’ views by having them compare their views to another country, and which institutions or groups of people that they deem relevant. In addition, this question led me to several my interviewees.

Transcription

All the interviews were fully transcribed. The Norwegian interviews were conducted in Norwegian, and the Finnish were conducted in English. The citations used in this thesis are already in English, meaning that I translated the relevant citations from Norwegian. In an analysis in which phrasing and words used are important factors, it is a limitation that both the Finnish interviewees had to translate their thoughts to English, and that the Norwegian interviews were translated too. However, the Finnish interviewees were all well-versed in

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23 English. Furthermore, I have sent the citations I wanted to use in the analysis to the interviewees. By doing this, they had the chance to correct mistranslations and make sure their points came across in a fair manner.

3.3 Data analysis

The selection of theory and analysis of data are the result of an abductive process, moving back and forth between theory and data. An abductive approach is both inductive and deductive, in that it entails an iterative process, alternating between theoretical concepts and the empirical findings. The goal of an abductive approach is to gain insight into a phenomenon (Alvesson & Sköldberg, 2008, p. 55). It is common to base the selection of theory on which ever can best explain the empirical findings. From then on, the scope of the data and theories applied are adjusted and developed by going back and forth between them.

Arriving at the specific research question, theory selection and analysis of data is the result of an abductive approach. In this case, the phenomenon that is being analysed is how policy- makers perceive and handle the emergence of technologies that have large potential risks. I started out with an interest in gaining more insight into this phenomenon regarding artificial intelligence and looked to the literature to find sufficient explanations. This led me to theories such as sociology of expectations, sociotechnical imaginaries, and co-production. These theories address how visions of the future shape policy today, as well as framing the limitations of future policies.

I wanted to analyse policy and deliberations in the making and found that Finland had both commissioned a report on existential risks and initiated a programme to outline strategy on AI in Finland. Subsequently, for reasons stated above, I chose to compare Finland with Norway, a similar country where there appeared to be less done on a national level in terms of AI strategy. I then proceeded to identify relevant reports, policy-makers, and other actors through methods described previously. This led me to the empirical data for this thesis: two proposed national strategies for AI, in addition to other publications and interviews with policy-makers and other relevant actors.

According to the abductive tradition, the data was coded and analysed in multiple iterations.

In the first coding, I was particularly interested in desired and undesired visions of the future,

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based on the theories of sociology of expectations and sociotechnical imaginaries.

Additionally, I looked for recurring themes in the data. The first round of coding produced a new set of codes that were more specific, namely what the role of technology in both cases are, which government measures are emphasised, the dynamic between society and the government, and how risks are perceived. I then turned back to the literature to try to explain the differences and similarities found in this new set of codes. This led me to technologies of humility, risk governance, and the importance of social scientists and including stakeholders.

Moving back and forth between the data selection and the theories, I realised that looking specifically at artificial general intelligence in the two strategies would allow me to study how global and catastrophic risks are perceived by the governments. This realisation prompted me to look beyond STS to futures studies to explain more of the empirical findings. This entire process resulted in the five main concepts presented in the theoretical chapter and the final research question of how Finnish and Norwegian policy-makers

i) perceive and approach artificial general intelligence and long-term consequences of the technology,

ii) how does this affect which policy-measures are deemed feasible and desirable, and

iii) what does this tell us about how to best approach AGI and its potentially significant and long-term consequences?

The five main concepts and research questions led to the final round of coding where the documents and interviews were analysed based on the following categories: how artificial intelligence and artificial general intelligence are defined, are they focusing on development or utilisation, what type of responses and measures are the two strategies proposing, to what extent are social scientists valued, and lastly, how are various groups presented as stakeholders, and are future generations represented among the perceived stakeholders.

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3.4 Quality and limitations of the data and analyses

3.4.1 Reliability

The reliability of a study depends on the replicability and transparency of the research. The goal is for other researchers to conduct the same research and get more or less the same results. For this to be possible, transparency of the research process is crucial. It is also important to be aware of the researcher’s role in the study. Although researchers should strive to be impartial in their work, it is difficult to be completely neutral (Tjora, 2012; Yin, 2016).

As Donna Haraway (1989) argues, researchers have certain situated knowledges that need to be disclosed. Situated knowledges are a researcher’s predispositions that might influence the research or analysis process in some way. Haraway (1989) suggests that researchers should be aware of their predispositions and make explicit how they might affect the research.

First of all, the fact that I speak Norwegian can affect my understanding on emphasis on different aspects and texts. For instance, I know more about the Norwegian context and there might be some things I have missed in the Finnish case due to lack of context. Further, it is possible that somehow emphasise the Norwegian case to a larger extent because it is closer to home. However, throughout the process I have tried to approach the cases symmetrically, for example by interviewing similar actors in each country and using the same interview guide.

Additionally, I have limited knowledge of artificial intelligence technology. I have tried to educate myself on the key aspects of AI, what it is and is not, and what are important facets of it. Artificial intelligence is both difficult to define and difficult to understand completely. This coupled with a lack of technological knowledge means that there might be aspects that I have inadvertently dismissed or misinterpreted. Moreover, I share many experts’ concerns with regards to the risks of artificial general intelligence. This can affect my ability to analyse the data impartially.

In order to counteract the influences of these predispositions, I have tried to be open and transparent about the research process and data analyses method. In addition, through using an abductive approach, I have let the data influence the choices of theory and focus. This will hopefully reduce the impact of my own beliefs and contribute to the analysis being more

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based on the data. Furthermore, in my representation of artificial intelligence, artificial general intelligence, and the risks of AGI, I have tried to present the case as fairly as possible.

3.4.2 Validity

To what extent research is valid depends on whether the analysis measures what is intended and whether the findings reflect reality. Internal validity is about whether the concepts and definitions being used can adequately describe the empirical data. As with reliability, transparency is important in evaluating the validity of an analysis. Through the abductive approach, I have been concerned with making sure that the theory used can explain the empirical findings.

Still, the validity of this study would greatly benefit from having more data. A more valid study would have reviewed more documents and interviewed more actors. In specific, this study could have been improved by including an interview with someone from the Board of Technology to elucidate some of the statements and points made in their report. Furthermore, the two strategies are proposed by institutions with different purposes. The Finnish strategy is from a Steering Group commissioned by the Ministry for Economic Affairs and Employment, while the Norwegian strategy is from the Board of Technology which is advising the government in the issues and opportunities of technology. Gathering sufficient data is particularly difficult in a topic as emerging and vibrant as artificial intelligence. As highlighted in the discussion chapter, the recommendations made in this thesis should be regarded as suggestions, and further research should be conducted in order to understand the two cases and their implications to a larger extent.

3.4.3 Generalisability

The generalisability, or external validity, of a study measures to what extent the findings are applicable to other cases. There are multiple types of generalisability linked to different methodological approaches. In more quantitative studies, the aim is often to generalise to populations, while in qualitative studies such as this one, the aim is rather to generalise to similar situations (Yin, 2016, p.284). Tjora (2012) promotes a form of conceptual generalisation, where the aim is to produce a set of concepts, typologies, or theories based on the analysis and previous works (p. 209ff). This level of conceptualisation is common in

Shaping the future

Shaping the future

A comparative case study of Finnish and Norwegian approaches to political strategy of

artificial intelligence

Eirin M. Evjen

Master’s Thesis

TIK Centre for Technology, Innovation and Culture Faculty of Social sciences

UNIVERSITY OF OSLO

Shaping the future:

A comparative case study of Finnish and Norwegian

approaches to political strategy of artificial intelligence

Abstract

Acknowledgements

Table of contents

1 Introduction

2 Science, technology, and futures studies

2.1 STS, artificial intelligence, and risk

2.2 Main concepts

2.2.1 Development versus application

2.2.2 Power of words

2.2.3 Humility and simplicity

2.2.4 A call for social scientists

2.2.5 Representation of future generations

2.3 Contribution to the literature

3 Methodology

3.1 Comparative case study

3.1.1 On case studies

3.1.2 Choosing Finland and Norway

3.2 Data collection

3.2.1 Public documents and reports

3.2.2 Qualitative interviews

3.3 Data analysis

3.4 Quality and limitations of the data and analyses

3.4.1 Reliability

3.4.2 Validity

3.4.3 Generalisability