Artificial Intelligence (AI). An Approach to Assess the Impact on the Information Economy

Research Paper (postgraduate), 2019
94 Pages, Grade: 1,0


Table of Contents

1. Introduction
1.1. Relevance and “Problem Puzzle”
1.2. Theoretical Approach
1.3. Structure of the Thesis

2. Artificial Intelligence
2.1. Intelligence and Artificial Intelligence
2.2. Different Types of AI
2.2.1. Weak AI vs. Strong AI
2.2.2. AI Definition for the Analysis
2.3. Place Value of AI in the Economy

3. Information Society
3.1. The Birth of a Concept
3.2. Basic Characteristics
3.2.1. The Rise of ICT
3.2.2. Employment Inside the Information Society
3.2.3. The Information Economy
3.3. Information Society and AI
3.4. Hypotheses

4. Methodology

5. Analysis of Strong AI in the Information Economy
5.1. Education
5.2. Information Services
5.3. Information Machines
5.4. Media of Communication
5.5. Research and Development

6. Interim Conclusion and Hypotheses
6.1. Evaluation of the First Analysis
6.2. Hypotheses for the Second Analysis

7. Supposed Consequences of Strong AI
7.1. Short- Midterm Szenario: Increasing Human-Machine Integration and Transformation
7.2. Mid- Longterm Szenario: Discussing e.g. Massive Unemployment, Inequality and other Security Issues

8. Different Models of Political Reactions
8.1. Retraining, Focus on Education and Prepare a New Workforce
8.2. Discourse of a Basic Income

9. Critical Review

10. Conclusion



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, organizations, 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 humankind 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 vast amount of data is therefore helping the growth and applications of so-called AI. Recent breakthroughs in the field of neural networks and deep learning algorithms as a part of machine learning, increase AI’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 already 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 work-force and thus societal change while also being seemingly helpless against uncontrollable and powerful digital players such as Google or Facebook. They are increasingly penetrating the so-called real economic sectors, also using more and more AI-applications and transforming the rules and fabric how actors engage in socio-economic relationships.

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 already transforming the way in which societies and economies are organized. 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 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 other, 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 societies.

However, looking at the current research, a consensus or clear-cut definition what might constitute AI precisely as a base for such a conceptual framework is missing. Further, only a little research has been conducted; understandingly given the relatively recent occurrence of digitalization 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 Digitalization. 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 recommendations to cope (or not to cope) with the effects of AI. Concerning the technology assessment of AI and the aforementioned technological 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 of AI technology, 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 increasing complexity research and a major challenge in the wake of the digital revolution (cf. Rouhiainen 2018; Gershenfeld et al. 2017).

However, the approaches established so far are merely based on comparing the respective AI with human intelligence. Others start in the past and compare technological milestones, financial expenditures within the sector in AI, ongoing research project related parameters to assess the current state of the art. One of several possibilities would be to distinguish in detail different types of neural networks or deep learning algorithms on the basis of exact scales, growth rates in terms of the speed of development of this technical field and finally 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 when the current state of the art is understood, and methods developed for that investigation can this problem puzzle be broken down and future impact AI assessed better.

Overall, it must be recognized that no consideration of the past helps and there is no suitable state of research. Therefore, 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 of 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.

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 employment in AI impacted segments of the economy. But also not a guarantee. Often it is argued that AI will eliminate jobs (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 unemployment. If so, a seemingly segmental transformation issue on a rather micro-economic level might turn into vast macro-economic and thus potentially 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 widespread? 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 economic segments, different questions shall be raised and different scenarios shall be discussed:

For example, what happens in case of rising mass unemployment or social inequality? Such a development could involve social instability, lost identity, profound disillusionment with “the political and economic system” 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 acceptance that parts of the labor force receive income from working while 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 AI will change the employment in the information society and what skills are important for a workforce in the age of AI. 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 equation.

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 political possibilities of actions in the final part of the research.

1.3. Structure of the Thesis

In the light of the above-described research approach, this thesis is structured as follows:

The second chapter will concentrate on AI. Here, a differentiation of AI into weak and strong AI will be introduced. Then it will look at the current state of deployment of AI. It will be discussed that economic actors actively and strongly already pursue the introduction of AI, seeking efficiency gains and higher profit margins. The following third chapter will develop the concept of the information economy as the economic dimension of the information society. Also, the evolution 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, AI 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 AI on economic activity in information societies.

In chapter fifth, the hypotheses will be applied to Machlup’s information society framework. Here, this theses looks at the industry sectors defined by Machlup. It will be argued, that almost all sectors will likely be deeply affected by AI. While chapter fifth takes a rather “micro” view on industry segments, chapter six again will develop further hypotheses, arguing that actions are required to alleviate the effects of AI. In chapter seven the analysis will change to a rather macro-political view. If many economic sectors are affected, what implications may arise for the entire information society and for the political sphere? Before discussing options for such actions, chapter seven will seek to further establish the validity of the action claim put forward by looking at already occurring impacts of AI. These short elaborations seek to foster the rather theoretical argument to Machlup’s framework with current evidence. Then, chapter eight discusses areas for actions to be taken, here proposing the need for further education for so-called knowledge workers, retraining the workforce of the future or a basic income. Finally, chapter nine will do a short critical review of the research so far, and then the conclusion will finish this thesis.

2. Artificial Intelligence

The beginning of the following chapter will concentrate on creating a definition of artificial intelligence and describes briefly what makes this emerging technology so powerful. Nowadays artificial intelligence is regularly used by people as shorthand to talk about everything from building robotic process automation tools, to chatbots, neural networks or deep learning (cf. Donovan 2017). The reappearing boom of AI in recent years in all things artificial intelligence catalyzed by breakthroughs in the area of machine learning. Machine learning can be defined as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty. It involves training computers to perform tasks based on examples, rather than by relying on programming by a human. In short, it is a self-teaching computer system (cf. Murphy 2012: 32; Piovesan/ Ntiri 2018: 1f.; Wired 2018; Fraser 2017).

The most relevant types of machine learning are supervised, unsupervised and reinforcement learning. The details of each of these methods are beyond the scope of this paper. Today these methods can be combined with a deep learning architecture, which makes machine learning approaches ways more powerful. Deep learning operates by using artificial neural networks. They contain layers of nodes that in some ways mimic the neurons in the human brain, to train computer systems on large quantities of data to recognize patterns in digital representations of sounds, images, and other data. Every single layer of neurons takes the data from the layer below it, performs a calculation, and provides its output to the layer above it. This architecture can be combined with an unsupervised process to learn the features of the underlying data, such as the edge of the face, and then provide that information to supervised learning algorithms to recognize features as well as the final result, which in the example of a photo of a human face correctly identifies a person in the picture (visual recognition). Besides these combinations, the most promising method is reinforcement learning combined with deep learning, so-called deep reinforcement learning systems (cf. Buchanan et al. 2017: 6ff., Murphy 2012: 32; Piovesan/ Ntiri 2018: 1f.). This combination is a powerful set of techniques used to generate control and action systems whereby autonomous agents are trained to take actions given an environment state to maximize future rewards. Though nascent, recent advances within this area are impressive. In addition to its recent victories in the game of Go, the software company Google DeepMind has achieved superhuman performance in several Atari games (cf. Fortunato et al. 2017). These short mentioned successful use-cases are notable technological milestones. Amongst other use-cases they have the ability to change the economic landscape, creating new opportunities for business value creation and cost reduction (cf. Esteva et al. 2017; Brynjolfsson et al. 2017: 3).

Intending to the potential of AI, in combination with recent improvements in big data, cloud or connected devices and the support of possible future technologies, such as quantum computers, this paradigm shift could be further stimulated. By that it is important to realize that the early leading adaptors of these emerging technological possibilities in the field of AI also take responsibility for their actions, even to make sure that everybody has a common idea what AI means exactly for individuals, organizations, companies, and societies.

The following chapter is intended to define artificial intelligence with the help of the definition of intelligence. After that, a differentiation of AI into weak and strong AI will be introduced. Then, a definition of AI for the following research should be made. The last part of this chapter will look at the current state of deployment of AI. It will be discussed that economic actors actively and strongly already pursue the introduction of AI, seeking efficiency gains and higher profit margins.

2.1. Intelligence and Artificial Intelligence

Starting with a definition attempt of artificial intelligence, it helps to differentiate AI from the basic concept of intelligence. So far, there are several different types of definitions, because intelligence exists at different levels and there is no consensus on how to distinguish them precisely. However, similarities can be detected under different definition attempts. In essence, it describes a general mental ability that includes the ability to discern rules and reasons, think abstractly, learn from experience, develop complex ideas, plan and solve problems. Artificial intelligence, in turn, is supposed to reproduce the aspects as mentioned earlier of human behavior to be able to act humanly in this way, without being part of a sentient organism. Thus it is intelligence that is artificially made. It includes qualities and abilities such as solving problems, explaining, learning, understanding speech as well as the flexible reactions of a human being (cf. Gentsch 2018: 17; Marwala/ Hurwitz 2017: 9). Since the middle of the 20th century, AI forms a field within computer science. The aim is to find methods by which human abilities such as the conclusion of experts, mathematical proofing, the recognition of images, the understanding of the natural language or the targeted optimization in any environment can be simulated on computers. These systems should be adaptive — for example, they could query an unknown fact to the user and to save the gained information for reuse. The stored programs and data can be represented in the form of rules (cf. Wedde et al. 1990: 280).

Since it is not possible to find the only one universal definition of Artificial Intelligence, the following definition by Elaine Rich seems to be the most appropriate for this research contribution:

”Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better.” (Ertel 2016: 2).

It briefly characterizes what scientists have been doing in the field of AI for the last fifty years and what they will do in the future. Humans are still more capable and suitable in most of the fields, but computers and algorithms already offer enormous advantages. Their ability being to dominate more fields in the existing society will increase. Further abilities of AI could evolve exponentially (cf. Appendix I). Other scientists say that AI is more like a theory and development of computer systems able to perform tasks usually requiring human intelligence. Alternatively, AI is an activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.

2.2. Different Types of AI

Overall, AI is a sophisticated technology with an equally complex conceptional understanding or without a universal definition of the term itself. In part, determining what AI is, it is only possible in its context. From a technical point of view, the types of AI are also enormously controversial and cannot be differentiated at first glance. For this purpose, the specific artificial intelligence must be closely examined and tested for their abilities.

So far, extensive tests focus on comparing human intelligence with artificial intelligence using, for example, Alan Turing's so-called Turning Test, widely used since 1950. He tests a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human (cf. Puget 2017).

An established AI Index or different types of distinguishing levels of AI itself is underrepresented. There are no clear procedures that could differentiate several AI algorithms with the help of specific scalability.

In order not to place the previously outlined technical complexity of today's AI systems in the focus of this chapter, the various differentiation and types of AI will be fundamentally differentiated into two types for this work: Weak and strong AI.

2.2.1. Weak AI vs. Strong AI

Weak AI or rather narrow AI describes an interpretation of AI according to which conscious awareness is a property of specific brain processes and whereas any physical behavior can, in principle at least, be simulated by a computer using purely computational procedures; computational simulation cannot in itself evoke conscious awareness (cf. Colman 2015). According to this view, the weak artificial intelligence is usually entrusted with concrete application problems, which do not depend on logical thinking, decision making or even not based on consciousness. It primarily serves humans as an information provider on which it bases their decisions. These include, for example, the following areas of responsibility: Expert systems, navigation systems, voice recognition or the automatic correction suggestions for digital search functions (cf. van der Touw 2016). Some common examples of weak AI are in consumer applications such as Apples Siri or Google Maps. It is combining several narrow AI techniques plus access to extensive data in a cloud (cf. Greenwald 2011). In shorthand, weak AI can be described as: „devices and applications that do specialist tasks much better than we ourselves could do them, mostly because they number-crunch in ways we can't.“ (Souter 2018: 1). However, it is still supervised programming, which means there is a programmed output or action for given inputs. So weak AI might behave like a robot or manufacturing line is thinking on its own.

Compared with that, Strong AI or Artificial General Intelligence (AGI) is a more complex approach that might change output based on given goals and input data.

“An interpretation of artificial intelligence according to which all thinking is computation, from which it follows that conscious thought can be explained in terms of computational principles, and that feelings of conscious awareness are evoked merely by certain computations carried out by the brain or (in principles at least) by a computer.” (Colman 2015b).

So a program could do something that it was not programmed to do when it detects a pattern and determines a more efficient way to reaching the goal it was given (cf. Kerns 2017: 2). The strong AI wants to create the most powerful computer systems imaginable. However, the utmost efficiency imaginable is the reproduction of human intelligence. In this way, strong AI can be described as the highest level of sapience or as machines with human-like intelligence and beyond (cf. Sarkar 2018: 1; Alpcan 2017: 2). „Strong“ is not this output of strong AI, what it already has created, which is in their own perspective rather "weak", but what it promises to deliver (Sesink 2012: 3f.). Regardless of this, one often speaks of strong artificial intelligence as when a machine has the same intellectual abilities as a human or even surpasses it. A perception like this would also mean that no longer only people are competing for a particular job with each other, but also the with strong AI itself as if it were a human worker.

Current examples for the early beginning of strong AI could be seen in deep reinforcement systems playing chess or go, soar cognitive architecture, autonomous vehicles or AI systems like Google Duplex. However, strong AI has not achieved his potential yet. Based on AI and its often quoted exponential character it can be assumed that strong AI might be relevant in the near future. However, it is still controversially discussed. While some argue the opposite that the full potential of strong AI will be a long-term achievement, others argue that it will never be reached.

Based on a few studies, some scholars predict that computers will reach human intelligence around 2029, because AI is an exponentially increasing technology and undergoing a massive acceleration driven by an immense growth especially in available data and the rapid evolution of algorithms. The full potential of strong AI, so-called real singularity or superintelligence of machines when they are more intelligent than humans at all, will come by 2045 (cf. Makridakis 2017: 11f; Kurzweil 2005). Furthermore, other scholars asked 60 experts of an Artificial General Intelligence Conference (2011) to answer the question, if they believe that AGI will be effectively implemented in the following timeframe. Of the experts surveyed, 43.3 percent estimated that it would be before the year 2030, 25 percent estimated between 2030 and 2049, 20 percent said between 2050 and 2099, 20 percent said after 2100, and 1.7 percent said never. In result, more than two-thirds of respondents predicted that AGI would occur before 2050. A more recent survey conducted in 2016 by Etzioni et al. was related to superintelligence, as an often mentioned ability of strong AI. Etzioni's question was based on Nick Bostroms book, which defined superintelligence as an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom, and social skills. Etzioni asked the experts when they think superintelligence could be a reality. The answers of the 80 responders are summarized as follows: Nobody thought in the next ten years, 7.5 percent estimated between the next ten and 25 years, 67.5 percent in more than 25 years, 25.0 percent decided to say never. These results put the start of superintelligence later than that of Kurzweil survey but still a realistic scenario (cf. Makridakis 2017: 11f.).

To return the perspective of this work and a possible qualitative investigation of strong AI, all in all, there are five areas that are often associated with strong AI: Awareness, self-perception, sensibility, wisdom, and feelings. However, it is still unclear to what extent these capabilities are interrelated. For example, if awareness is necessary to think logically (cf. van der Touw 2016).

Based on these basic descriptions strong AI should be seen in the course of this research with the following characteristics: Logical thinking, natural language, self-learning, self-structured planning and decision-making in uncertainty.

In conclusion, there are significant differences between the two types of AI. The term Strong AI refers to the type of AI that has specific capabilities similar to those of human intelligence. This concept differs significantly from the existing computing systems and mainstream AI algorithms, already described as weak AI. Weak AI-based systems are successful in solving specific problems when the problem context is provided directly by a human programmer. In contrast, strong AI is much deeper and broader in its ambition as it aims for human-like flexibility, understanding, and creativity (cf. Alpcan 2017: 1f.).

2.2.2. AI Definition for the Analysis

The following course of this research will focus on strong AI. Although strong AI is not being used today as predicted by its potential, some scholars expect that the necessary technical developments will evolve rapidly in the next years. By looking into empiricism, tendencies and the early beginning of strong AI could already be identified. Examples are deep reinforcement learning systems winning in chess or go against the human world champions, AI systems like Google Duplex or autonomous vehicles. Nevertheless, against the background of the critical literature as well as the current technical limitations of strong AI, a weak form of strong AI should be used for the analysis of this research. The core of this definition will thus be formed concerning the previous chapters and by its current state of the art. So the basic understanding of strong AI for the following analysis within this research will be described as follows:

In general, build on weak AI because it is still working in the economy, but also the AI system has to be described at least with one of the following characteristic of strong AI: Logical thinking, natural language, self-learning, self-structured planning and decision-making in uncertainty. Thus, the definition would theoretically be located between weak and strong AI, but by describing it with one of the mentioned characteristics should mean the system is already an early stage of strong AI. The more of the mentioned characteristics would be fulfilled the more advanced would the strong AI become.

For example, deep reinforcement learning systems winning in chess or go against the human world champions, because of such increasing successes, at least the self-learning characteristic would be evaluated as realistic. Looking at systems like Google Duplex, the natural language characteristic could be considered as given. In the case of autonomous vehicles, however, several properties of strong AI could already be considered. Both self-learning, self-structured planning and decision-making in uncertainty would be conceivable. Thus, autonomous vehicles would undoubtedly be a showcase example of strong AI within the mobility sector. Based on these examples, it can also be argued that they already go well beyond the complexity measure of weak AI and can, therefore, be seen as the early form of strong AI.

2.3. Place Value of AI in the Economy

Andrew Ng, Professor of Stanford University, coined the phrase that AI seems to be the new electricity. From his perspective, AI will revolutionize all industries of the economy in a similar way to the electrification process of the world (cf. Tomer 2017; AI100 2017: 54). This metaphor is symbolic of current developments and potentials in the field of AI. Artificial Intelligence is undergoing a massive acceleration driven by an immense growth especially in available data and the rapid evolution of algorithms.

The following chart shows most of the different industry sectors. It measures, on the one hand, the current AI adoption, and on the other hand, the future AI demand trajectory.

Figure I: Sectors leading in AI adoption today also intend to grow their investment the most.

Abbildung in dieser Leseprobe nicht enthalten

Source: Forbes 2017.

Financial services, high tech and telecommunications are the leading early adopters of Artificial Intelligence and will be the leading industries that adopt AI in the next two years. In addition to this chart, it is also noteworthy that financial services1 and healthcare2 seeing the highest increase in their profit margins as a result of AI adoption (cf. Forbes 2017). Within ten years AI and robotics are expected to create an estimated annual so-called creative disruption impact of up to 33 trillion dollars globally. It includes eight trillion to nine trillion of cost reductions across manufacturing and healthcare, nine trillion in cuts to employment costs due to AI-enabled automation of knowledge work and 1.9 trillion in efficiency gains via autonomous cars and drones (cf. Medium 2017). It is predicted that AI may power the next generation of efficiency tools (cf. Motte 2017).

The leading technology firms are in a race to build the best AI and capture a massive market. Thus IBM is working on its Watson, Amazon is banking on Alexa, Apple has Siri. At the same time Google, Facebook, and Microsoft are devoting their research labs to AI and robotics. All of these companies show their public attitude and are highlighting their enthusiasm for Artificial Intelligence (cf. Kelnar 2016; Bajpai 2017; The Guardian 2017; Maney 2016: 1). At the same time, investments in the field of AI are rising considerably. For example tech giants including Baidu and Google spent between 20 billion to 30 billion dollars on Artificial Intelligence in 2016 alone. It includes 90 percent on R&D and deployment, ten percent on Artificial Intelligence acquisitions (cf. Data Collective 2017; Medium 2017; Forbes 2017).

“The last 10 years have been about building a world that is mobile-first. (…) In the next 10 years, we will shift to a world that is AI-first.” (Rieber 2017: 13).

Practitioners place a high expected future value on Artificial Intelligence. The current age of Artificial Intelligence has the potential to disrupt the world’s largest industries and is expected to contribute 15,7 trillion us dollar to the global economy by 2030. That is one reason why AI is often called a new wave of innovation or the world’s next industrial revolution (cf. Hindu Business Line 2017; Piovesan/ Ntiri 2018; Appendix II). AI and the widespread adoption of cognitive systems across a broad range of industries will drive worldwide revenues from nearly 8 billion dollars in 2016 to more than 47 billion dollars in 2020 with banking named as one of the top two industries to lead the charge (cf. IDC 2016). This estimate and assumption based on several parallel developments, which will not be discussed in detail in this research. However, to name a few, the following illustration helps.

Figure II: Development of Infastructure.

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Source: Narrativ Science 2017.

These technological achievements, increasingly powerful hardware, evolving machine learning approaches, and vast new data sets are fueling a transformation of these major global industries such as financial services, healthcare, retail, education, manufacturing and supply chain, mobility or the security sector (cf. Data Collective 2017; Medium 2017).

All in all, the potential of AI in the business sector can be seen in the increase in profit margin and productivity. Based on a McKinsey research (2017) the following chart shows that AI is a way to increase profit margins:

Figure III: AI adopters with a proactive strategy have significantly higher profit margins.

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Source: McKinsey 2017: 21.

The orange graph shows AI adopters with a proactive strategy, which include firms that are using big data and cloud services and share their strategic posture towards AI.

Furthermore, AI is seen as a productivity-enhancing factor. A Bank of America Merrill Lynch report predicted that adoption of robots and AI could boost productivity by 30 percent in many industries while cutting manufacturing costs by 18 to 33 percent (cf. Medium 2017).

By that, the McKinsey found out:

“companies who benefit from senior management support for AI initiatives have invested in infrastructure to support its scale and have clear business goals achieve 3 to 15 percentage point higher profit margin.” (Forbes 2017).

Furthermore, the following chart estimates the increase in labor productivity with AI, more precisely the percentage difference between a baseline without AI improvements and an AI steady state in 2035:

Figure IV: Projected increase in labor productivity with AI.

Abbildung in dieser Leseprobe nicht enthalten

Source: Medium 2017.

Accenture and Frontier Economics underlines with their comparative country illustration that AI can be seen as a robust productivity-enhancing factor (cf. Medium 2017). With much of today's businesses making use of AI for the task of increasing their productivity, even a new job title of a “Chief AI Officer” has been introduced to manage the use of AI in the most efficient way (cf. Forbes 2017b; Schrage 2017: 1). In this context, it underlines the importance of using AI as a productivity-enhancing technology with optimal impacts in each company.

In the healthcare sector, an architecture using deep neural networks was tested against 21 board-certified dermatologists and matched their performance in diagnosing skin cancer (cf. Esteva et al. 2017). In the software sector, the social networking company Facebook uses their neural networks for more than 4.5 billion translations every day. For example, image recognition is one reason amongst others, why an increasing number of companies have responded to these opportunities (cf. Brynjolfsson et al. 2017: 3).

Figure V: Image Recognition, Vision Error Rate.

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Source: Brynjolfsson et al. 2017: 3.

The image recognition algorithms reduced their own error rate between 2010 and 2016 significant. Since 2015 it is first lower than the error rate of humans, which offers enormous impact. Another common use-cases are natural language processing, which refers to a system’s ability to process and understand human language to convert it into full representations. It holds enormous benefits as well and is for example used in chatbots, real-time text translation or new systems like google duplex (cf. Murphy 2012: 32; Piovesan/ Ntiri 2018: 1f.).

In conclusion, it can be assumed that AI has a revolutionizing impact. However, the whole development of AI in the economy and this desired increase in productivity and profit margin of the companies has far-reaching consequences, which will be analyzed in a later part of this research.

3. Information Society

The information society is a highly abstract and diverse discussed concept of modern industrialized countries, which meaning and development have been described very differently in the past. There is a consensus in the literature that the concept of modern information societies is due to massive societal changes from the second half of the 20th century until modern days, but no consensus when exactly individual countries entered this age of society. The term is also referred to knowledge society, post-industrial society, network society or information age. Depending on the focus of the research and the interests involved, various characteristics are identified and presented for the information society. In general, the term information society can be characterized as important social developments linked to the use of information as well as information and communication technology. Based on a few definitions which seem suitable for the purpose of this research, information society is described as a form of economy and society in which the extraction, storage, processing, communication, dissemination and use of information and knowledge, including growing technical possibilities of interactive communication, plays a crucial role (cf. Kamps 1999: 245; Beniger 1986: 4ff.; Webster 2014; Martin 2005: 4f.)

However, based on the fact that the typology of the information society is so different, the following illustration will cover the different approaches.

Figure VI: The typology of information society theories.

Abbildung in dieser Leseprobe nicht enthalten

Source: Fuchs 2012: 415.

Shown by this illustration the information society theory discourse can then be theoretically categorized by making use of two axes. The vertical axis distinguishes aspects of societal change, the horizontal axis the informational qualities of these changes (cf. Fuchs 2012: 414f.).

3.1. The Birth of a Concept

From today's scientific perspective, the most relevant ideas of the information society were already formulated from 1960 to 1980 and elaborated within the framework of concepts of knowledge and post-industrial society. At the heart of these concepts lay the analysis of the influence which the increasing role of information and information technologies exerted on the social and economic changes within the societies of that time of Western Europe, USA and Canada or Japan (cf. Kasperkiewicz 2004: 309). Essential theoretical foundations for the information society put the American economist Peter F. Drucker in its work 1969 under the designation knowledge society, based on a previous book from 1962 of Fritz Machlup „The Production and Distribution of Knowledge in the United States“. Machlup described the beginning of a so-called information economy and most people agree that he started it all, although he used the term knowledge industry and not information society. However, he showed that the production of knowledge is an economic activity and is describable with the terms used in the analysis of the industrial sector. In addition to Machlup, Drucker coined the term of the so-called knowledge worker in 1967. He predicted the emergence of a knowledge-based society based on a major increase in the average level of education. As a result of educational expansion in the US, knowledge became a key factor in production, and the knowledge worker the most important figure in the American workforce (cf. Klotz 2009: 4; Duff 1996: 118; Reinecke 2010: 4; Drucker 1969; Karvalic 2007: 6). Later, in his book 1969, he based the concept of the knowledge society on the central thesis that knowledge has become the very basis of the modern economy and society or the real principle of social action (cf. Drucker 1969: 326, Engelhardt 2010: 22).

However, information society was not only the subject of economics or information systems but also of sociology. Daniel Bell, American sociologist and professor of Harvard University from 1970 to 1990, coined the term of the post-industrial society since 1958 and is recognized to be the foremost writer on the information society. It is argued that Bells position has always contained three distinct elements (cf. Klotz 2009: 4; Duff 1998: 373; Webster 2006).

“One relating to the post-industrial information workforce, a second dealing with information flows (particularly scientific knowledge), and a third concerning computers and the information revolution. Bell’s information society thesis is best understood as a synthesis of these elements.” (Duff 1998: 373).

He explains this in „The Coming Post-Industrial Society“ from 1976, which can be considered paradigmatic for the idea of a profound structural change in industrial society (cf. Bell 1976; Steinbiecker 2011: 50).

In regard to the birth of the exact expression of information society, the concept is also linked to the Japanese terms „Joho Shakai“ and „Johoka Shakai“. Joho shakai is usually translated into English as the information society but has also been rendered as an information-oriented society, information-conscious society and information-centered society. Johoka Shakai has a sense analogous to industrialized society but is also translated sometimes merely information society. Against this backdrop, the term first emerged in Japanese social sciences in the early 1960’s. The first English language reference dates are from 1970 and have to be linked to Yoneji Masuda, who used the expression in his lecture at a conference and appeared in print in the same year. Moreover, in 1971 a systematizing dictionary on information society was published from Johoka Shakai Jiten and many Japanese publications followed (cf. Karvalics 2007: 5f; Duff 1996: 118f.).

„the realization of a society that brings about a general flourishing state of human intellectual creativity, instead of affluent material consumption“ (Masuda 1980: 3).

Masuda interpreted information society as a positive development for humanity, although many new challenges are imminent. More recently but also relevant for the concept of the information society is the sociologist Manuel Castells, the renown sociologist of Berkeley University and author of the groundbreaking book of the so-called Information Age. He claims by long-standing research in more than thirty countries of the world that a new kind of society comes into being - the so-called network society. Castells presented 1996 to 1998 three research publications on this Information Age. First „The Rise of the Network Society“, second „The Power of Identity“ and third „The End of the Millennium“. His extensive research has been in the wake of capitalist restructuring and based on the revolution in information and communication technologies (cf. Castells 1996: 28ff.). In contrast to Drucker's post-capitalist society and Beil's post-industrial society, Castells speaks of a rejuvenated, informal capitalism based on informationalism, a new mode of development (cf. Castells 1996: 77; Steinbiecker 2011: 79f; Kasperkiewicz 2004: 310).


1 AI and advances in robotics will likely disrupt millions of workers within these sectors globally, generating up to 50 percent in productivity gains.

2 AI-based services will play a growing role in automated support, diagnosis, and advice in healthcare (cf. Medium 2017).

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Artificial Intelligence (AI). An Approach to Assess the Impact on the Information Economy
Helmut Schmidt University - University of the Federal Armed Forces Hamburg
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Künstliche Intelligenz, KI, Artificial Intelligence, AI, Information Economy, Technologiefolgenabschätzung, Technikbewertung, Informationsgesellschaft, Information Society, Technology Risk Assessment, Technology, Bedingungsloses Grundeinkommen, Basic Income, Mensch-Maschine Interaktion, Human-machine-interaction, Digitalisierung, Industrie 4.0.
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Henry Alexander Wittke (Author), 2019, Artificial Intelligence (AI). An Approach to Assess the Impact on the Information Economy, Munich, GRIN Verlag,


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