Content

1. Introduction in:

Henry Alexander Wittke

Artificial Intelligence, page 1 - 5

An Approach to Assess the Impact on the Information Economy

1. Edition 2020, ISBN print: 978-3-8288-4459-9, ISBN online: 978-3-8288-7480-0, https://doi.org/10.5771/9783828874800-1

Tectum, Baden-Baden
Bibliographic information
1. Introduction 1.1. Relevance and “Problem Puzzle” An ongoing and seemingly unstoppable digital transformation brings about new options, opportunities but also challenges to individuals, or ganizations, companies and societies alike. The everyday gets more and more influenced by drivers like the creation of gigantic amounts of data. Now the total amount of data being produced doubles every year. In 2016 the world produced as much data as in the entire history of human kind through 2015. It is estimated, that in ten years, the available amount of data will double every twelve hours (cf. Helbing et al. 2017: 2). In the wake of exponentially rising data, also the field of Artificial Intelligence (AI), is developing significantly. The increasing availability of a vast amount of data is therefore helping the growth and applications of socalled AI. Recent breakthroughs in the field of neural networks and deep learning algorithms as a part of machine learning, increase A I’s potential to disrupt the world’s largest industries. For example, in the business sector, AI is poised to have a transformational impact. Although it is al ready in use in most companies around the globe, most big opportunities for AI deployment have not yet been tapped. Current developments in the field of AI alarm governments, seeing the potential consequences on the workforce and thus societal change while also being seemingly help less against uncontrollable and powerful digital players such as Google or Facebook. They are increasingly penetrating the so-called real eco nomic sectors, also using more and more AI-applications and transform ing the rules and fabric how actors engage in socio-economic relation ships. From a scientific perspective, there are different perceptions of AI, which will be categorized and also simplified within this thesis into weak and strong AI. It is assumed that weak AI-driven automation is al ready transforming the way in which societies and economies are orga nized. But the impact and transformation caused by the beginning of strong AI and its deep learning algorithms could be much more profound than changes origination from weak AI. As with any profound change, there will be players winning from this transformation but also losers. Vast transformation processes are not new. But the difference this time under strong AI is that most observers feel that job losses in established sectors will occur at an unpreceded level, while only relatively few new jobs will be suitable or created for the same work staff at all. It is further 1 being argued that another difference to previous technical transformation is as follows: Technological advancement destroys low-skilled jobs. Higher education would secure new jobs in different sectors. However, this time, it could not be the case. Even the highest skilled employees could end up with machines and systems doing their work. Instead of „transformation“, i.e. a switch from resources from one sector to the oth er, there will just be: idle human resources, further caused by a huge skill gap. And this on a massive scale. An imaginable scenario like this caused by strong AI will strongly influence the so-called information society, basic principles of capitalism and the foundations of today's so cieties. However, looking at the current research, a consensus or clearcut definition what might constitute AI precisely as a base for such a conceptual framework is missing. Further, only a little research has been conducted; understanding^ given the relatively recent occurrence of dig italization using AI and the few available results vary strongly. On the one hand, differentiation between strong and weak AI are done weakly or not at all, using general perceptions of Computerization or Digitaliza tion. On the other hand, warnings on the effects of strong AI are being often made without intending to provide detailed insights into the precise effects. Possibly, only once we have understood and developed a concept of AI separating AI from other digitalization trends can we estimate the impact of AI on workplaces, economies, and societies and provide rec ommendations to cope (or not to cope) with the effects of AI. Concern ing the technology assessment of AI and the aforementioned technologi cal upheavals, the identified research gap seems to be essential to be filled. The main problem with AI is the lack of measurability of change. Based on the literature and the complexity of AI itself, it can be seen that there are no quantitative measuring methods, instruments or indices for both the current state of the art and the possible uses of strong AI. “Without the relevant data for reasoning about the state o f AI tech nology, we are essentially “flying blind” in our conversations and decision-making related to AI.” (AI100 2017: 54). This research gap has to be closed in the future. The need has already been identified, but against the background of the exponential character of AI, other Big Data-based technologies are often described as increas 2 ing complexity research and a major challenge in the wake of the digital revolution (cf. Rouhiainen 2018; Gershenfeld et al. 2017). However, several approaches established so far merely base on compar ing AI with human intelligence. Others are comparing technological milestones over time, the rise of financial expenditures within the AI sector, ongoing research projects or related parameters to assess the cur rent state of the art. There is a lack of standardized methods for example to distinguish different neural networks or deep learning algorithms based on exact scales, growth rates in terms of the speed of develop ments of the technical field as itself to track the societal transformation. Pioneering projects that address this research gap include, for example, the scientists working on the AI Index at Stanford University with other research facilities. Only if the current state of the art is better analyzed, and methods developed for that investigation this problem puzzle might be solved and future impact of AI better assessed. Overall, we recognize that no consideration of the past helps to solve the real challenge and there is not enough suitable state of research. There fore, the following theoretical approach should be proposed. 1.2. Theoretical Approach This thesis seeks to investigate, which effects in particular strong AI could have on our today’s societies. For this undertaking, a conceptual framework is required. As Steven Hawkings said: “The rise o f powerful AI will be either the best or the worst thing ever to happen to humanity. We do not yet know which.” (Ingham 2018) To seek to investigate if AI might be the best or worst thing to happen, this thesis attempts to provide a basic framework using concepts of strong and weak AI and thus to make a small contribution to the initial research available today. The concept of the Information Economy as the economic dimension of the information society shall form the generic base of the methodological approach. The Information Economy concept is widely acknowledged as a way to describe highly developed large economies. Further, it can be adjusted to the purpose of this investigation and enriched by the notion of strong AI. 3 To then attempt to estimate the impact of AI on economies, we use Machlup’s segmentation of economies in the information society age: Education, Information Services, Information Machines, The Media of Communication, Research and Development. These five segments shall be used as research objects to discuss the impact strong AI might have on them beyond weak AI or common digitization trends. Special notion is placed on employment and labor skills since without taking the argument too far already here - it is assumed that highly developed labor skills are a necessary precondition for employ ment in AI impacted segments of the economy. But also not a guarantee. Often it is argued that AI will eliminatejobs (cf. Brougham/Haar 2017). From here follows the next reason to emphasize the effects on labor and employment. If it can be argued in the case studies that all of Machlup’s five economic segments could be strongly affected by AI in terms of job losses, then an entire economy might face substantial rises in unem ployment. If so, a seemingly segmental transformation issue on a rather micro-economic level might turn into vast macro-economic and thus po tentially political problem. Thus, based on the results of the case studies, this thesis will switch to a more macro-level approach. Which economic and political impacts could entire societies face if the effects of AI are profound and wide spread? What will be the effect on work and labor as a value system for individuals and societies? In this second analysis following the analysis on Machlup’s eco nomic segments, different questions shall be raised and different scenar ios shall be discussed: For example, what happens in case of rising mass unemployment or so cial inequality? Such a development could involve social instability, lost identity, profound disillusionment with “the political and economic sys tem” or even riots. Could the entire notion of capitalism be questioned in the wake of strong AI? Which responses might politics and societies as a whole develop? Which social solutions or strategies on a large-scale are needed for upcoming social challenges? We will look at two possible options: On the one hand, a basic income as an approach to alleviate the effects of unemployment on individuals but also as a new way labor and income distribution could be organized: The allocation of a secure income to unemployed persons and thus the ac ceptance that parts of the labor force receive income from working while 4 others are free to pursue ends and goals with a basic income without working in traditional contractual employment schemes. On the other hand, we will look at skill development, at the so-called information worker and show how strong Al will change the employ ment in the information society and what skills are important for a work force in the age of Al. Retraining and educating this workforce seems to be very important, and the human-machine symbiosis could be essential. It will discuss how people can work together in an optimal relationship with robots and AI-driven applications. Tasks getting done by machines and others by people could be even smarter than either side of the equa tion. Based on these discussions, the thesis then attempts to draw conclusions and first initial recommendations to policy makers. There might be many complex and intertwined ones, not feasible to handle in this thesis. All in all this upcoming paper will contribute to this goal by discussing politi cal possibilities of actions in the final part of the research. 1.3. Structure ofthe Thesis In the light of the above-described research approach, this thesis is struc tured as follows: The second chapter will concentrate on Al. Here, a dif ferentiation of Al into weak and strong Al will be introduced. Then it will look at the current state of deployment of Al. It will be discussed that economic actors actively and strongly already pursue the introduc tion of Al, seeking efficiency gains and higher profit margins. The fol lowing third chapter will develop the concept of the information econo my as the economic dimension of the information society. Also, the evo lution of Information and Communication Technology (ICT) and the change of work, as well as the information worker, will be described. In the last part of the chapter, Al will be transferred into the context of the information society, and the hypotheses for the analysis of this research should be made. This is followed by the development of hypotheses to start to produce an assessment of the impact of Al on economic activity in information societies. In chapter fifth, the hypotheses will be applied to Machlup’s information society framework. Here, this thesis looks at the industry sectors defined by Machlup. It will be argued, that almost all sectors will likely be deep ly affected by Al. While chapter fifth takes a rather “micro” view on in dustry segments, chapter six again will develop further hypotheses, argu ing that actions are required to alleviate the effects of Al. In chapter sev en the analysis will change to a rather macro-political view. If many 5

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Abstract

The ongoing and seemingly unstoppable digital transformation brings forth new options, opportunities but also challenges to individuals, organizations, companies and societies alike. Governments are alarmed, realizing the potential consequences on the workforce, while also being apparently helpless against uncontrollable and powerful digital players such as Google or Facebook. As Henry Wittke shows, recent breakthroughs in the field of machine learning increase the potential of Artificial Intelligence to disrupt the world’s largest industries. Wittke attempts to provide a basic framework of what constitutes AI as well as to assess its impact on the Information Economy. What happens in case of rising mass unemployment or social inequality? What will be the effect on labor as a value system for today’s societies? Could the entire notion of capitalism be questioned in the wake of AI? The book aims to draw conclusions and give recommendations to policymakers.