An automation tax would hinder their development

Keynote: In competition with robots? Perspectives of TA on autonomous systems

Keynote: In competition with robots? Perspectives of TA on autonomous systems

Michael Decker

Robots can be described as a combination of sensors, actuators and control software. This general description fits “classic” industrial robots and so-called service robots that are used outside of industrial halls. Ambitious research directions aim at cognitive, social, learning, autonomous and artificially intelligent robotics, which can be seen as the physical "front end" of an Internet of Things or digital change.
Otherwise, we only attribute cognitive performance, social behavior, learning, autonomous action and intelligence to people. The competition between robots and humans has long since begun: chess, jeopardy and games of go are considered highly recognized (thinking) sports among humans, high-precision welding and precise positioning are difficult skills, and humanoid robots - some of which are confusingly similar to humans - challenge our image of man in a different way.
Competition in the labor market has been described by Frey and Osborne for the US labor market as being very tough on human workers. Autonomous driving and technical support in care are areas of application that are currently particularly addressed in the public debate. Both the cooperation between humans and machines and the complete replacement of humans - in certain contexts of action - are considered.
 
In the introduction, some threads of discussion in technology assessment of robotics that have emerged over the past 15 years will be outlined. Using case studies on care robotics / Ambient Assisted Living (AAL) and autonomous driving, current research questions are addressed and, in particular, machine learning is thematized as a future research focus of technology impact research.
 
Michael Decker is Professor of Technology Assessment at the Karlsruhe Institute of Technology. Since 2004 he has been a member of the ITAS institute management. He is currently head of the "Computer Science, Economics and Society" department at KIT. He is chairman of the "Innovation and Technology Analysis" advisory board of the Federal Ministry of Education and Research (BMBF) and of the "Technology and Society" advisory board of the Association of German Engineers (VDI). In addition to technology impact research on robotics, his research interests include the theory and methodology of technology assessment (TA) as well as concepts for inter- and transdisciplinary research.
 
• Frey / Osbourne (2013): The future of employment: How susceptible are jobs to computerization? (http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf)

• Decker, M .; Fischer, M .; Ott, I. (2017) Service robotics and human labor: A first technology assessment of substitution and cooperation. Robotics and Autonomous Systems 87, pp. 348-354

• Decker, M .; Gutmann, M .; Knifka, J. (2015; Ed.) Evolutionary robotics, organic computing and adaptive ambience. Epistemological and ethical implications of technomorphic descriptions of technologies. Vienna: LIT

Keynote: Totally digital - threat or opportunity? Change in work and effects on employment and quality of work

Keynote: Totally Digital - Threat or Opportunity? Change in work and effects on employment and quality of work

Annika Schönauer

Digitization is enjoying a triumphant advance and is shaping the world of work and private life in a completely new way. This applies to the field of industry as well as service work and consumption. Digitization or the term “Industry 4.0” stand for a sustainable change in the world of work, but also in private life. American studies predict revolutionary upheavals and drastically falling employment figures. Other experts emphasize that serious estimates of the employment effect are hardly possible. One thing is clear: incremental innovations and the continuation of rationalization and automation measures lead to a fundamental change in work and make an important contribution to the dynamics of the labor market. However, the development must be viewed in a more differentiated manner than is currently usually the case. It seems essential not to discuss digitization as an inevitable “force of nature”, but as a process that has to be shaped socially and politically. In addition to questions of employment and qualifications, there are also labor and socio-political challenges to be considered. Whether digitization becomes a threat or an opportunity is a matter of design.
 
Annika Schönauer is an occupational sociologist and member of the management team of the research and advice center for the world of work, FORBA, in Vienna. Her research interests include internationalization and flexibilization of work, digitization of work and working hours.
Together with others, she is the author of part of the 2016 Austrian Social Report on Work 4.0 and recently published several texts on digital, location-independent work.
 
• Flecker, Jörg / Riesenecker, Thomas / Schönauer, Annika (2016): Work 4.0. Effects of technological changes on the world of work. In: Social Report 2015 - 2016, BMASK. https://broschuerenservice.sozialministerium.at/Home/Download?publicationId=372
 
• Flecker, Jörg / Schönauer, Annika (2017): The Production of ‘Placelessness’: Digital Service Work in Global Value Chains. In: Jörg, Flecker (Ed.): Space, Place and Global Digital Work. Palgrave. http://www.palgrave.com/de/book/9781137480866
 
• Schörpf, Philip / Flecker, Jörg / Schönauer, Annika (2017): On call for one’s online reputation - control and time in creative crowdwork; In: Briken, Kendra et al. (Eds.): The New Digital Workplace. How New Technologies Revolutionize Work, Palgrave, Critical Perspectives on Work and Employment. https://he.palgrave.com/page/detail/the-new-digital-workplace-shiona-chillas/?sf1=barcode&st1=9781137610133

'Digital Taylorism': For the computerization of company processes

'Digital Taylorism': For the computerization of company processes

Eva-Maria Raffetseder

Most companies are inconceivable without computer-controlled work processes. Especially with the hype surrounding the startups, which demanded “quickly scalable structures”, providers of process management software celebrated the boom. Workflows are automated and decisions are transferred to the machine using algorithms and the definition of threshold values. So is it time to talk about the "second industrial revolution" prophesied by Martin Heidegger - marked by "the input of the decision into the machine"?
Salesforce is a program that shows this comprehensive development that all areas of life and work are exposed to: recording and regulation by algorithms. These two concepts also represent the core elements of the CRM ("Customer Relationship Management") software Salesforce: On the one hand, the actions of the person interacting with the system are measured, on the other hand, it is a matter of predicting and calculating the actions of a third party (the customer) to model and influence possible behaviors and courses of action. The two central questions that arise here are:
• How is human behavior in all its contingency translated into non-contingent systems?
• To what extent must human work be assumed to be “programmable” (that is, controllable in minute detail) so that “algocratic control” can be implemented by process management systems?
Salesforce records all activities within the system, “knows” everything about the services already provided (or not provided) by employees and also offers forecasts such as the percentage probability that a contract will be concluded with a certain customer.
What companies hope for by using Salesforce software is optimization: more contracts, lower error rates, higher customer satisfaction and less effort, which above all means: less time required.
This article aims to show the symbolic order that has manifested itself in the algorithms implemented in the media. The analysis of the technical aspects of Salesforce should not get stuck on the phenomenal surface of the software, but examine the system with a media-archaeological view. This media archaeological method asks about the relationship between a media artifact and the associated epistemes.
With reference to the work of Frederick W. Taylor, Frank B. Gilbreth and Stafford Beer, the epistemes of recording as well as the regulation and optimization of work processes in the context of process management systems will be shown on the special case of Salesforce.
 
Eva-Maria Raffetseder: 2008 to 2011 Bachelor's degree in "Communication, Knowledge, Media" at the Upper Austria University of Applied Sciences. Master's degree in media studies at the Humboldt University of Berlin from 2012 to 2016. Since October 2016 doctoral candidate in the MCTS Post / Doc Lab Digital Media at the Technical University of Munich. PhD project on “Programming Labor. How Process Management Systems Reconfigure Sociotechnical Assemblages. ”(Focus on the influence of process management systems such as SAP and Salesforce on work processes and organizational environments).
Research interests: media archeology / media theory / history of digital media / techniques and technologies of process management

Mode of the future: learn from the utopias of the past for the digitized and automated working world of the future?

Mode of the future: learn from the utopias of the past for the digitized and automated working world of the future?

Petra Schaper-Rinkel

With the digitization and automation of the future world of work discussed today, a different social future is anticipated than was designed in utopias and disruptive futuristic scenarios of the past. Utopias of the past have often associated technological innovations with a 'better future' and designed new technologies as a motor for equitable distribution of consumption and work. Digitization, automation and the possibilities of the data-driven future Internet of Things appear technically more fantastic than all past attempts at anticipation. In contrast, the social and political imagination for the development and use of technical innovations seems stunted. Today the digitalization of the future working world is imagined by its advocates less as a provocative, revolutionary social innovation than as an optimized continuation of the present: more efficiency, more competition, privatization, individualization and economization instead of a provocative social futility at the thought-experimental level of technical innovations.
Can foresight and TA broaden the methodological and content-related horizon for the innovation paths of the future by looking at the utopias of the past? The article investigates this question. For example, would it be technically possible with the digital platforms as they are used in today's crowdworking, which remained an empty space in many utopias: to organize the distribution and the coordination of demand and demand quickly, cooperatively and efficiently?
There are different dimensions of utopias that are back on the agenda today in a new form. In the early modern period, Thomas More countered brutal exploitation and exclusion with a fair distribution of work and consumption that was oriented towards material restrictions. In Tomaso Campanella's sunny state, agricultural work is rationalized and organized collectively so that everyone can devote themselves to science and art, and Francis Bacon comes up with the idea of ​​a science-based production. The French Revolution of 1789 and its failure show both the possibility of fundamental change and its limits. The utopias of this time are phenomena of the transition to industrialization and the scientification of the future: In Henri Saint-Simon's economically liberal utopia, the state is responsible for ensuring framework conditions for production so that market players can produce efficiently. For Charles Fourier, rationalization is only the material prerequisite for new forms of coexistence. The British manufacturer Robert Owen built exemplary factories and stood for the scientification of the utopian ideal: his goal was decentralized productive cooperatives that were to produce a maximum of wealth with a minimum of unsatisfactory work with the least possible resources. State and politics disappeared into the economic organization of the future.
With their different concepts of continuous progress, these utopias form the theoretical counterpoint to the imponderables of politics, which are characterized by revolutions and the respective restoration. Edward Bellamy's idea from 1887 is that the abstract idea of ​​progress in combination with rationalization and technical innovations will lead to a global fairness of distribution in which all people have the same annual income and thus realize very different life plans Theoretical inventor of the credit card and much of what remains technically unspecific for him is technically available today with digitization and global networking.
These examples of past futures up to the end of the 19th century represent designs for the transformation of contemporary social and political rationalities. They could provide starting points for thinking experiments on the development of the digital economy of the future.
 
Petra Schaper-Rinkel is a political scientist and Senior Scientist at the AIT Austrian Institute of Technology. Main focus: governance of future technologies, societal futures and foresight.

A TA perspective on working in the digital age

A TA perspective on working in the digital age

Tanja Sinozic, Johann Čas

The study of the relationship between technological progress and unemployment has a very long tradition and, until recently, there was broad consensus among economists that technical innovations create more jobs than they destroy in the long term. Recently, however, this consensus seems to be becoming fragile, based on indications that under current conditions central innovations will not lead to a sufficient number of newly created jobs. The much-cited study by Frey and Osborne from 2013 contributed to assigning this topic a high priority in the political agendas of numerous EU countries and triggered a number of further studies on this question. Although the research approaches and results vary widely, job losses are mainly associated with the coding and computerized processing of individual tasks in routine activities in the service sector, with automation and robotics in the production sector, with work mediated via Internet platforms and the associated changes in business models. These changes raise a number of pressing questions for securing prosperity, maintaining labor standards or adequate training and further education.
The European Parliamentary Technology Assessment Network EPTA published a report last year that provides an up-to-date overview of current developments in the field of work and digitization in 17 regions and countries participating in this network. This article will address selected key findings, national debates and political paradigms that influence the relationship between digitization and the labor market. Finally, frequently mentioned economic policy measures in connection with digitization are critically discussed.
 
Dr. Tanja Sinozic is Senior Researcher at the Institute for Technology Assessment (ITA) of the Austrian Academy of Sciences. She is currently researching the use of smart grid technologies in Austria. She previously worked at the Vienna University of Economics and Business and in the Science Policy Research Unit (SPRU), University of Sussex.

Digitization as the engine of industrial renewal in Austrian companies?

Digitization as the engine of industrial renewal in Austrian companies?

Susanne Giesecke, Bernhard Dachs

Our paper deals with the current industrial change through digitization using the example of Austrian industrial companies.
The aim of the study is to gather new insights into the spread of digital technologies in industry in order to improve the information basis for decisions on this topic. It is about digitization as well as new products, services and business models that can result from it.
In our empirical work we examined the following aspects:
• How important is digitization as a motor for industrial renewal?
• What is the level of knowledge and skills as a prerequisite for digitization in companies?
• Are the demands of digitization on companies and the world of work flanked by appropriate political measures?
The findings include, among other things, that there are opportunities for new services in companies, but that no effects on entire value chains have yet been felt. Although their potential is rated as high, new business models, in which services also play a larger role, are still in their infancy. Many companies see the affinity of young employees to new information technologies as advantageous, while the older ones are perceived as inflexible and as resistance. Based on this analysis, the question arises of what politics, companies, social partners and employees can do to develop new business models, modernize the value chain and achieve an integrative and sustainable change to digitization that is affordable for everyone.
The study is part of the Finnish project iREN “Innovation Research 2014: Renewal of Manufacturing”, which examines and compares several countries and their digitization efforts. In addition to Austria and Finland, this also includes Germany and the USA. Over 200 experts from industry and politics were interviewed as part of the project.
 
Dr. Susanne Giesecke has been working as a Senior Researcher at the Austrian Institute of Technology AIT GmbH (formerly Austrian Research Center) since 2005. Here she coordinates the topic “Foresight”. Her current focus is on qualitative innovation research, technology policy and also technology assessment, evaluation of research programs and future studies / foresight. For the latter focus, she is active in several EU projects, e.g. for the preparation of the next research framework program as well as for health policy and social innovations in a leading position.
 
Dr. Susanne Giesecke, Center for Innovation Systems & Policy, Austrian Institute of Technology,
Email: Susanne.Giesecke [@] ait.ac.at.

Dr. Bernhard Dachs, Center for Innovation Systems & Policy, Austrian Institute of Technology,
Email: Bernhard.Dachs [@] ait.ac.at.

Digitization of the social economy - innovation potential and risks for social service companies

Digitization of the social economy - innovation potential and risks for social service companies

Richard B. Handel

Based on preliminary work for a doctoral project, the submitted contribution serves to approach the question of how a research program can be designed that deals with the effects of digitization on service organizations in the social economy and the associated innovation potentials and risks of this development. Despite the economic importance of the social economy, a specific discussion of digitization is only just beginning to be discernible.
Digitization should be understood as a central form of social innovation. According to Howaldt and Schwarz, the core of social innovation consists in the intentional reconfiguration of social practices in certain fields of action, with the aim of bringing about an improvement. The new is not simply to be found in technical artefacts, but in new social practices based on them and resulting from them.
Understood in such a normative and intentional manner as social innovation, the digitization of the world of work presents significantly more opportunities than terms such as the “digital tsunami” suggest. This perspective is important in order to recognize the positive potential and creative element of these changes. Nevertheless, the ambivalence of the point-dependent evaluation of the effect of digitization as a technical as well as social innovation should be taken into account when considering, because "there is no inherent goodness in social innovation". For example, digital inequality research shows that technical innovations and the associated possibilities do not necessarily lead to a leveling of inequalities, but can even intensify them.
The conception is based on the question of whether and which digitization models exist in the social economy and what consequences these have above all for the employees of the service organization and the service recipients, but also for the organization providing the service itself.
Previous research suggests that established service companies focus on the administrative potential of ICT technologies, industry software, and web presence. The innovative potential of this adaptation to changes in society as a whole appears to be rather low.
The use of digital technologies (AAL, robotics) to expand social services, on the other hand, seems to have greater potential to actually make work easier, but is usually developed on the basis of technological feasibility and thus easily ignores needs. This triggers fears of increasing control by third parties, rationalization and dehumanization of services among employees and users alike.
The development of innovative applications of digital technologies, on the other hand, is more likely to be observed in SocialStartUps. Their offers address unmet needs and, through technology-supported forms of work, open up new perspectives, e.g. for the long-term unemployed or people with disabilities.
Our own research results on socially innovative services suggest a connection between the origin, size and age of the organization, the working conditions and the needs orientation for employees and users. Employees play a special role in this. Under sometimes precarious conditions, they are the organizational contact point for users. Because of their professional qualifications and experience, they know the needs of your clients best. At the same time, it is precisely their working conditions that change in the course of digitization processes - after all, they play the main role as mediators in the implementation of new technologies. Therefore, it is not only necessary to examine which models of digitization exist in which companies, but above all what consequences these models have for employees and their employment relationships, as well as the services provided.
At best, it is not enough to make necessary adjustments to changing social conditions that are compatible with all those involved. Rather, the overarching objective is to sound out the innovation potentials and risks of digitization for the organizations of the social economy in order to activate the former and avoid the latter, and thus contribute to strengthening social service providers and overcoming societal challenges (shortage of skilled workers, increased need for care and support, Demographic change etc.). A prerequisite for this is a differentiated approach to the topic that follows the basic attitude that digitalization is also a process that can be shaped, which must not be legitimized based on technical feasibility, but based on social benefit.
 
Dipl.-Soz. Richard B. Handel is a research associate in the organizational pedagogy department at the Faculty of Education and Training at the University of Trier. There he conducts research on the promotion of innovation in social enterprises and the digitization of the social economy. Before that, he worked for several years as a research assistant at the Evangelical University of Darmstadt and in non-profit management.

Unsteady constellations - competence management in SMEs under the conditions of advancing digitization and automation

Unsteady constellations - competence management in SMEs under the conditions of advancing digitization and automation

Veit Hartmann, Robert Tschiedel

The lecture provides interim results from an ongoing project on the subject of "PROKOM 4.0 - Competence Management for Skilled Work in the High-Tech Industry", sub-project "Competence Management for Business Associations", which was carried out by the Federal Ministry of Education and Research (BRD) from 1/2014 to 12 / 2017 is funded.
Extensive literature work and our own primary surveys show that previous competence management (almost exclusively) as an adaptation of the competencies of employees to new machine requirements and reorganization of the human-machine interfaces must be replaced or at least supplemented by short-term “re-orchestration” of human-machine -Constellations to achieve the operational purpose of value networks. This is increasingly taking place in project-based (also global) networks.
The background is the fact that in the course of advancing digitization and automation, additional competencies (can) be transferred to machines and that this also includes evaluations and decisions made by machines (automats). In addition to human and machine competence, we also have what we call “artificial competence”. Competence management is thus becoming a completely new task, namely to link the competencies of core workforce, external workers (crowdworkers, freelancers), own and third-party machines and automats, algorithms, etc. with the appropriate competencies of other companies in each project-related manner and parallel and consecutively new constellations (with ) in order to achieve their own operational purpose within the framework of a joint purpose.
 
Small and medium-sized companies in particular are rarely prepared for this, namely in addition to the competence to achieve their own operational purpose, to develop “network competence” in this sense and to keep it up to date. Competence management is often (if at all) organized as personnel development, independent of the technical development and independent of (also inter-company) organizational development. In addition to the new requirements for skilled work, there are also new requirements for management and company organization, which will be particularly highlighted here. A low-threshold survey instrument for the determination, determination and initiation of necessary operational development is presented.
The question of how corporate social responsibility (CSR) strategies can be appropriately and effectively integrated (implemented) in the process of increasing digitization and automation is also of particular importance to the authors; this is one of the other main topics of the TAT.
The lecture will have the following structure:
 
1. What is it about?
2. Concept of competence and competence management so far
3. Artificial competence and the management of constellations
4. Implementation of corporate social responsibility (CSR)
5. What follows from this? (1) Innovation-oriented technology assessment
6. What follows from this? (2) Management network literacy
7. Outlook: automated competence management?
8. Summary.
 
The authors / lecturers have been involved in a large number of projects and functions with work-oriented modernization and (innovation-oriented) technology assessment for many years in the project companies of the Transfer Center for Adapted Technologies in northern Münsterland (NRW, BRD). Numerous publications.
 
Further information can be found at www.tat-zentrum.de.
 
Veit Hartmann holds a master's degree in sociology and a degree in ergonomics. He works as a project manager in Rheine in North Rhine-Westphalia (BRD) in the TAT Technik Arbeit Transfer gGmbH. After many previous ones, he is currently head of the above-mentioned BMBF project.
 
Dr. Robert Tschiedel is an adjunct professor of sociology at the University of Münster (sociology of technology and methods of empirical social research) and founder and long-time director of the TaT Transfer Center for Adapted Technologies in Rheine in North Rhine-Westphalia (BRD) and the local research institute TAT Technik Arbeit Transfer gGmbH.
Among other things, he was the longstanding chairman of the Technology Assessment and Assessment Working Group of the State of North Rhine-Westphalia.

Trust and Industry 4.0

Trust and Industry 4.0

Thilo Hagendorff

The increasing digitization of the world of work is summarized under the buzzword Industry 4.0. Various advantages and disadvantages of this development for employees and employers are highlighted (Morlok et al. 2016). Regardless of the advantages, i.e. increased efficiency, flexibility in the choice of working hours and work location, simplification of work steps, etc., disadvantages can also be mentioned. For employees, for example, there is a heightened feeling of surveillance or an increasing dedifferentiation between professional and private life. There are also disadvantages for employers, including the increased vulnerability of the information technology infrastructure of their own company due to limited IT security or the risk of a loss of trust among employees.
The last-mentioned aspect should be picked out at this point and examined in more detail. Trust has a very fundamental influence on social cohesion within groups of people, here within companies (Frings 2010; Mayer et al. 1995). Social cohesion or social cohesion is characterized by a socio-emotional "we-feeling". As a result of good social cohesion, more or less strong mutual obligations and mutual responsibility relationships are entered into. Relationships of trust require that communication and cooperation processes run 'smoothly', i.e. that these processes accompanying control and testing tasks are reduced or can be omitted entirely (Frevert 2013, p. 104 ff.). However, what the trend towards Industry 4.0 is triggering is precisely an increase in those control and testing routines through the increased use of digital monitoring technologies in the workplace. These take the form of sensors or software for measuring productivity, behavior, personality, mood, etc. of employees (Buchhorn 2014; Martin and Freeman 2001; Nolan 2003).
Surveillance goes hand in hand with fears of sanctions, which admonish the observance of specified norms and rules (Fuchs 2011). Depending on the subject of the standard, this can be ethical, but it can also suppress innovations and social changes - which can be important and desirable for a company. Since the digitization of the world of work is ultimately tantamount to enforcing the same with monitoring technologies, there is a risk of establishing a pronounced culture of mistrust within companies (Möller 2012). This counteracts essential dynamics for building trust. Trust makes it possible to build up expectations regarding mutual good behavior on the basis of the suppression of moments of uncertainty (Luhmann 1973). However, this uncertainty, which is necessary for building trust, is eliminated by surveillance technologies. The slow process of collecting information about the trustworthiness of other people, which precedes the established trust relationships, is replaced by technically mediated monitoring and control routines.
The function of interpersonal trust relationships is to make contexts of action more productive. As mentioned, trust lowers the transaction costs of social relationships. In this way, in turn, cooperation relationships can be facilitated and promoted. In this way, trust strengthens the stability and performance of social contexts. This performance, which is generated by the genuinely social mechanism of interpersonal trust building, is fundamentally thwarted under the conditions of a working world permeated by manifest or latent surveillance technologies. What is actually regarded as an advantage of Industry 4.0, namely the increase in performance and productivity, is in a certain sense turned into the opposite, particularly those industries that are dependent on innovation and creativity are affected.
In summary, the lecture is intended to analyze the complex of Industry 4.0, monitoring technologies and trust within companies.
 
Thilo Hagendorff (born 1987) studied philosophy, cultural studies and German literature in Konstanz and Tübingen. He is currently studying media studies. Thilo Hagendorff received his doctorate in 2013 with a sociological thesis on "Social criticism and social control" (summa cum laude). Since 2013 he has been a research assistant at the International Center for Ethics in Science and since 2014 a lecturer at the University of Tübingen. His main research areas are digitization, social media, big data, privacy, data protection, surveillance, virtual reality, media ethics, technology ethics, and information ethics.

 

Are machine-recognized emotions suitable triggers for action in technical systems?

Are machine-recognized emotions suitable triggers for action in technical systems?

Gerd Lindner, Torsten Fleischer

Systems that recognize emotions are a prominent element in digitization strategies and in technical approaches to new human-machine interactions.For example, they are intended to provide psychiatrists with easily interpretable measures in order to be able to diagnose better. In the context of automated driving, the state of the driver should be recorded, on the one hand to derive needs and intentions and on the other hand to be able to determine whether and when the vehicle control can be handed back to the driver. In classrooms appropriately equipped with sensors, information about the emotional state of the students should be evaluated in order to predict their performance, to recognize the need for intervention and, ideally, to be able to intervene automatically.
We examined technical systems that draw conclusions about the emotional state of a user via external markers that can be recorded by sensors, such as facial expression, voice pitch and body posture, and derive direct consequences or recommendations for action from this.
Emotion concepts. Concepts such as emotion, anger, fear and grief are mixed up in a complex way that has evolved behavioral patterns as well as socially influenced norms and rules of behavior. There is a whole series of sometimes highly complex and abstract theories of emotion, but no scientifically recognized and unambiguous definition. In the current development, the need to deal with this is bypassed in many places through the use of machine learning processes.
Machine learning. Through the use of machine learning processes, a definition of emotions finds its way into technical systems indirectly and sometimes unnoticed. The assumption of the existence of five to seven universal basic emotions (according to Ekman) is promisingly simple and compatible with machine learning processes. The use of this learning process promises to be able to identify the core of what constitutes the corresponding emotions fully automatically from data sets provided with appropriate labels. The large number of current studies in this area shows that statistical relationships between externally perceptible markers and emotion words that are valid within the underlying data sets can be successfully identified. However, it is controversial to what extent these relationships are generally valid.
Relationship between emotional expression and internal state. The null hypothesis that there is no connection between facial expressions and emotions can be viewed as refuted. However, there does not necessarily have to be a causal connection between fundamental emotional processes and externally perceptible expressions. In addition, there are great intercultural and interpersonal similarities in emotional experience and behavior, but there are also differences. The latter concerns, for example, the emotion triggers and also the conventions that people follow when trying to control their facial expressions in social situations.
Emotion recognition without the inclusion of contextual knowledge. Can an emotional expression be properly classified without knowledge and understanding of immediately preceding events and other people involved, as well as without a basic understanding of the "rules of the world"? Here we reach the boundary between weak and strong artificial intelligence.
Conclusion. The promises made by developers as to what emotion-sensing systems are capable of doing in everyday practice and what appears to be feasible in the foreseeable future are still far apart. On the one hand, this is due to the challenges and difficulties mentioned above. In addition, it should be borne in mind that today's technical systems usually implement a quasi-linear decision-making model that does not adequately take into account interaction and human verification in the control loop of emotion-recognizing systems. However, this would be necessary due to the complexity and variability of human emotionality, the challenges with regard to the correct contextualization and the inherent unreliability of machine learning processes. From this it can be concluded that such systems should at best be used as an automated element of more complex hybrid decision-making systems, but should not be able to make decisions with significant consequences in an unsupervised manner.
In addition, side effects and dangers of the widespread use of such systems have so far only been insufficiently considered. One could think of the possibility of unlearning how to deal with one's own emotional states if emotion-adaptive systems take on this task. The question of to what extent a conviction evoked in the user with regard to the reliability of emotion recognition damages the trust in one's own emotional sensorium should also be investigated further.
 
Gerd Lindner is a master's student in computer science at the Karlsruhe Institute of Technology. His focus is on machine learning, with his areas of interest bordering on biology, psychology and sociology.
 
Torsten Fleischer is head of the research area "Innovation Processes and Technology Consequences" at the Institute for Technology Assessment and Systems Analysis (ITAS) at KIT. His research interests are in TA and innovation research in “enabling technologies” and their applications.

Big data and discrimination in the world of work

Big data and discrimination in the world of work

Ingrid Schneider
 
Digitization and big data are not only changing the content and organizational forms of work, but also the way people are recruited and their work is assessed. Algorithmic methods of qualification assessment and predictive analytics are already being used in job assessment centers and when selecting job applicants. Big data could serve to reveal existing discrimination and to make access to jobs more egalitarian. But big data also has the potential to exacerbate discrimination by introducing unequal treatment into more and more areas of society and pretending to make personality assessments predictable. The pursuit of efficiency and effectiveness in decision-making is driving the expansion of big data not only for personnel recruitment, but also for evaluating work performance. This is countered by ethical demands on the preservation of human autonomy (Zuboff 2015), social solidarity (Rouvroy 2016) and fairness (Ulbricht / Schneider 2017).
Personal profiles can be created on the basis of social media data, for example. For example, one study combined Facebook “likes” with demographic profiles and some psychometric tests. In 88% of the cases, this revealed the sexual orientation of men, religious affiliation (Christian or Muslim, 82%), skin color (white or African-American, 95%), political orientation (Democrat or Republican, 85%), and correctly assign alcohol and cigarette use (between 65% and 75%) (Kosinskia et al. 2013). Assessment centers also use sentiment analyzes (tone of voice, mood, vocabulary; emotions) and calculate probabilities for depression and other physical illnesses (White House 2016). Do these lead to more "objective" selection processes or rationalize and "objectify" discrimination ?
A number of studies show that big data applications provide gateways for prejudice and biases in data sets can easily lead to the continuation of discrimination. Historical prejudices or imbalances can inadvertently be perpetuated in data sets: inputs or results from the past are reproduced in the outputs of an algorithmic system. For example, in the case of employment relationships, previous recruitment patterns in which mothers were not included in the selection or were only given part-time jobs can be updated if the algorithm correlates the number of children and gender with data from the previous recruitment policy (Barocas and Selbst 2016: 689). People who identify themselves as female in their Google advertising settings and then search for jobs on Google are less likely to receive ads for career advancement than male users (Datta et al. 2015).
Age or gender discrimination in personnel selection is prohibited under anti-discrimination laws. Indicators that allow statements about gender and age can, however, be used as a proxy (proxy) and thus conceal discriminatory preferences (Hofstetter 2016: 380), for example on ethnic origin, gender or sexual orientation (Barocas / Selbst 2016: 692f).
In the German-speaking countries, profiling and knowledge about sensitive personal information have so far been discussed primarily under the subject of privacy and data protection or surveillance. In the USA, the debate is more focused on the question of which social inclusion and exclusion potentials are associated with big data analytics. This debate found expression in two reports from the White House in 2014 (White House 2014) and 2016 (White House 2016) and the Federal Trade Commission (2016). Their analyzes and results, insofar as they relate to the potential for discrimination in the world of work, are to be presented. In addition, it should be asked whether and to what extent these analyzes can be transferred to Europe. In addition, it is about whether and how illegitimate unequal treatment that violates legal equality laws can be proven. Because big data analytics is often used, among other things. protected with the reference to trade secrets and thus remains intransparent.
 
Prof. Dr. Ingrid Schneider is Professor of Political Science and has been part of the "Ethics in Information Technology" department of the Computer Science Department at the University of Hamburg since January 2017. From 2002-2016 she was a research associate in the medicine research group of the BIOGUM research area (biotechnology, society and the environment) at the University of Hamburg. She works on TA and democracy theory, policy analysis, governance and legal regulation, digitization, big data and data protection, health, discrimination, competition law, intellectual property and the regulation of platform industries.
E-mail: Ingrid.Schneider [@] uni-hamburg.de
 
Literature:
• Federal Trade Commission (FTC) 2016. Big Data. A Tool for Inclusion or Exclusion? Understanding the Issues. FTC Report, January 2016
• Kosinskia, Michal / Stillwella, David / Graepelb, Thore (2013)., Private Traits and Attributes Are Predictable From Digital Records of Human Behavior, Proceedings of the National Academy of Sciences, 110, 5802ff
• Rouvroy, A. (2016). Council of Europe, “Of Data and Men” Fundamental Rights and Freedoms in a World of Big Data, Bureau of the Consultive Committee of the Convention for The Protection of Individuals with Regard to Automatic Processing of Personal Data
• Zuboff, S. (2015). Big other. Surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30 (1), 75-89
• Datta, A., Tschantz, M. C., & Datta, A. (2015). Automated Experiments on Ad Privacy Settings. Proceedings on Privacy Enhancing Technologies, 2015 (1)
• White House (Executive Office of the President) (2014). Big Data: Seizing Opportunities, Preserving Values, May 2014
• White House (Executive Office of the President) (2016). Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights, May 2016
• Hofstetter, Y. (2016). The end of democracy. How artificial intelligence takes over politics and incapacitates us. Gütersloh: C. Bertelsmann
• Ulbricht, Lena / Schneider, Ingrid (2017). Is Big Data Fair? Normatively established expectations of Big Data, in: Heil, R. / Kolany Raiser, B. / Orwat, C. (Ed.): Big Data and Society. A multidisciplinary approach. Wiesbaden: Springer (forthcoming)

Relaunch of the TATuP magazine and news from the openTA specialist portal

Relaunch of the TATuP magazine and news from the openTA specialist portal

Jonas Moosmüller, Ulrich Riehm

The lecture provides information on the current state of development during the relaunch of “TATuP - Journal for Technology Assessment in Theory and Practice” and the DFG-funded project “OpenTA specialist portal”.
Publish high-quality technology assessment and bring it into the social discussion - TATuP has been appearing with this claim for 25 years. In order to do justice to it in the future, the journal published by the Institute for Technology Assessment and Systems Analysis (ITAS) will be renewed in 2017 in several ways:
* TATuP is scientifically upgraded through the introduction of an assessment process.
* TATuP will be available online as an open access journal.
* TATuP will continue to appear in print and in a new, sophisticated design.
* In future, TATuP will be supervised by the oekom publishing house (production, sales, marketing) - the editorial team will remain at ITAS.
* TATuP is more closely linked to the TA (NTA) network through its editorial board and a scientific advisory board.
As part of the DFG-funded project “Fachportal openTA”, all articles previously published in TATuP are recorded and the archive is made completely available in the openTA publication service. The subject of the lecture is also the current possibilities of using the publication service and the planned and developed function extensions. The publication data made available by the cooperating institutional members of the NTA will in future be enriched by linking them to publicly accessible authority databases.

With 'high quality human components' to the new AI

With 'high quality human components' to the new AI

Doris Allhutter

The aim of research on Artificial Intelligence (AI) was and is still to equip machines with the ability to “understand” defined problem areas in a similar way to humans and to further develop this ability automatically through independent learning. The availability of large amounts of data ("big data") and research into how the meaning ("semantics") of information and resources can be translated into machine-readable form have opened up new perspectives in AI. A central question, however, is how high the costs are for preparing relevant knowledge in such a way that it can be processed and structured by machines.
This contribution examines the work of building the socio-technical infrastructure, on the basis of which new approaches of AI and machine learning are developed. While epistemological narratives of automation are made strong in the research areas covered, the “connection of scalable, automatic and high-quality human components”, i.e. semi-automatic processes, is used again and again in implementation practices. Online platforms such as Amazon Mechanical Turk and CrowdFlower have opened up the promising opportunity to make use of the “wisdom of crowds” or “collective intelligence” in this area as well. For several years, the potential of “harnessing human computation” has been explored in various computing communities, and appropriate methods, practices and prototypes are being developed.
In my lecture I analyze how crowdwork is discussed and implemented as a technical problem-solving approach and how human work is made invisible. In contrast to the expert systems of early AI, the decentralized architecture of semantic approaches intends to integrate a plurality of worldviews. In order to be able to capture this plurality, logical inconsistencies must be discovered and corrected. “Human computational power” is necessary in these areas in particular. However, my lecture shows that the incentive, job evaluation and remuneration mechanisms that are used in crowdwork systems have the effect of restricting the desired plurality and fading out alternative knowledge structures.
 
Doris Allhutter is a science and technology researcher and holds a doctorate in political science at the Institute for Technology Assessment (ITA) of the Austrian Academy of Sciences. She holds an Elise Richter position funded by the FWF and deals with the politics of information technology.

When machines learn to learn - human-machine communication between trial-and-error and deep learning?

When machines learn to learn - human-machine communication between trial-and-error and deep learning?

Stefan Strauss

Machine learning (ML) has always played a central role in the field of artificial intelligence (AI). ML deals with the development of automated, adaptive processes to enable computer systems to acquire empirical knowledge (cf. Mitchell 2006). Early ML approaches were strongly characterized by methods that were more like forms of trial and error than systematic approaches to solving complex problems. This is shown by the following quote from the early AI researcher and transhumanist Marvin Minsky: "The most central idea of ​​the pre-1962 period was that of finding heuristic devices to control the breadth of a trial-and-error search" (Minsky 1968, 9). A growing variety of developments and applications in the field of ML can be observed in recent years. It is therefore obvious that ML and its fields of application are considerably more advanced today. More recent approaches of so-called "deep learning" (DL) enable powerful analyzes and efficient restructuring of large amounts of data in order to identify patterns in them and learn them step by step.The approach is based, among other things, on artificial neural networks in order to analyze information on the basis of multiple levels of abstraction (see LeCun / Benigo / Hinton 2015). Companies like Google and Facebook are promoting research in this area and making a significant contribution to the spread of deep learning. Without a doubt, ML or DL ​​offers considerable potential for the development of autonomous systems that can massively change the world of work. However, the question arises whether the high performance is not primarily due to improved computing power and automated algorithms, and machine learning approaches continue to be based on the statistical calculation of probabilities, as they were in the beginning. In this respect, one can further ask to what extent ML risks with regard to the automated updating of normalization processes andarithmetically contains negligible errors. Against the background that learning machines can increasingly find their way into society and the economy, the question of what effects this development has on the interaction between humans and machines arises. Alan Turing's famous test named after him (Turing 1950) takes on a different meaning here. The article takes a look at different developments in the field of machine learning and in particular sheds light on the questions raised using empirical examples. One focus is on aspects and areas of tension in the context of human versus machine autonomy.
 
Stefan Strauss is a research associate at the Institute for Technology Assessment (ITA) of the Austrian Academy of Sciences. He is originally a business IT specialist and, in addition to his research activities, also has experience in software development and ICT-guided participation processes. At the ITA he deals with the social effects of information and communication technologies, in particular e-governance and political processes, identity management, surveillance, security and the protection of privacy. Further research interests lie in the field of information and computer ethics. Participation in several international research projects, e.g. on electronic democracy, digital identity, cloud computing and social networks; Security and privacy, protection of critical infrastructures. Current publications on e-participation in Europe, privacy impact assessment and the tension between security and privacy.

Collaborative robotics as an example of human-machine interaction: opportunities for more inclusion in working life?

Collaborative robotics as an example of human-machine interaction: opportunities for more inclusion in working life?

Karsten Weber, Sonja Haug, Thomas Schlegl, Clemens Pohlt, Johannes Höcherl

The use of collaborative robotics in the industrial environment and the associated new forms of human-machine interaction pose new challenges in terms of learning how to use technology, but also in terms of the usability of technology in general. The contribution combines two of the central questions of the conference: How is the interaction between man and machine changing? What skills will be needed in the future? This should also address the question of whether partial automation can provide an opportunity to improve the inclusion of older workers or people with disabilities in industrial production.
The study is based on an intelligent workstation (Smart Workbench, SWoB) developed by the Regensburg Robotic Research Unit * and an industrial partner, which is intended to support people in manual handling tasks and to carry out certain production processes in a semi-automated manner. The activity therefore requires less force, which is a good prerequisite for the inclusion of people with limited performance and ability to work. However, familiarization with the new technology and a change in previous work processes must take place, which could result in barriers. Based on empirical studies, the article examines which challenges and opportunities can arise.
First, an experiment was carried out to learn how to control a robot with three study groups and two learning methods to test a gamified learning tutorial. The demonstrator developed within the sub-project consists of a technically upgraded manual workstation and a collaborative lightweight robot. Communication takes place non-verbally by means of gesture control. In order to be able to operate the system, a briefing must take place in advance, which can be carried out by people or through a learning tutorial with gamification elements to increase motivation. The study examined which form of instruction is more likely to be accepted or rejected by different people and for what reasons. The test subjects are made up of young people with disabilities, older people and younger people. The tests were intended to find out whether automated guidance has advantages over human guidance. There were advantages, for example through less time pressure and a lower error rate, but also the challenge of adapting to different learning levels.
Second, an operational test of the partially automated workplace (SWoB) in an industrial environment was used to examine the extent to which participatory methods of technology development can be expedient and whether problem areas arise in the implementation of new forms of human-technology interaction in everyday work. After a test run, different groups of people were questioned qualitatively. One dilemma is that it is difficult to involve stakeholders at an early stage without a functioning prototype that can be used in a time-saving manner, but on the other hand hardly any test in a development stage in which the human-technology interaction can hardly be changed can have more influence on the technical design.
With regard to the perspectives, it should be discussed to what extent human labor will increasingly be replaced by robotics in the future or whether new forms of collaborative robotics can lead to a more humane design of the working world.
 
Prof. Dr. Karsten Weber is co-director of the Institute for Social Research and Technology Assessment (IST) at the OTH Regensburg and honorary professor for culture and technology at the BTU Cottbus-Senftenberg.
Prof. Dr. Sonja Haug is professor for empirical social research and social informatics as well as co-director of the IST at the OTH Regensburg.
Prof. Dr. Thomas Schlegl teaches handling technology and robotics, control technology and optimization, actuators and the basics of electrical drive technology at the OTH Regensburg and is head of the Regensburg Robotics Research Unit (RRRU).
Clemens Pohlt, M.Sc., is a research fellow at the RRRU and researches the robust classification of image sequences for natural human-machine interaction.
Johannes Höcherl, M.Sc., is a researcher at the RRRU and researches safe and efficient human-robot cooperation with a focus on intelligent robot behavior adaptation and situational operator relief.

Informatized, networked & efficient? Digital working environments and their consequences

Informatized, networked & efficient? Digital working environments and their consequences

Bettina-Johanna Krings, António Moniz, Linda Nierling

There is no question that the debate about the digitization of industry has “all the characteristics of a 'hype'” (Hirsch-Kreinsen et al. 2015: 9). With the programmatic formula of Industry 4.0, it is technically about the "merging of production techniques with information technologies" (Aichholzer 2016: 29), that is, the comprehensive networking and communication of data inside and outside of production. The choice of the term Industry 4.0 refers to the widespread notion that fundamentally new production regimes (should) arise in the course of the use of digital technologies. If these developments are interpreted from a socio-economic perspective, then these visions certainly tie in with well-known phenomena that can be described by a “comprehensive networking of industrial value chains” (Aichholzer 2016: 26, Huws et al. 2009).
Although the mentioned technical developments are currently attracting a great deal of attention from industry, the technical possibilities of information-based technologies have already become deeply inscribed in the world of work and have shaped the institutional, operational and individual premises of gainful employment to a large extent. "Digital and networked work is now practically done in all sectors of the economy" (Schwemmle, Wedde 2012: 16), be it in the office, in production, in sales or in service. In many work areas, a “socio-technical space for action” (Rammert, Schulz-Schäfer 2002) has long been created, which to this day can hardly be surpassed in terms of dynamism (Krings 2013). The use of information-based technologies takes place in different functional orientations and ranges from the transformation of material into immaterial goods to the coordination and communication of data flows to the change of entire professional profiles.
The question of how to evaluate these all-encompassing processes largely takes place within the framework of the paradigm of technical progress, which means that efficiency and productivity gains in work processes are largely the inspiration for promoting these developments. This orientation is part of a long tradition of socio-economic embedding of work in the institutional framework of the overall social organization of work.
The present contribution pursues the thesis that digitization processes as a (new) socio-technical space of action in different work contexts are not examined systematically. Be it in trade, in health care or in public administration: work processes are continuously being digitized, which means that extensive automation concepts are sought and change the interaction between man and machine, but also the organization of working environments to a great extent. The systematic recording of these technical consequences could make important contributions to questions of high social relevance. These relate to the consequences and limits of automation in work contexts. While work was still recognized as a key category (Offe 1984) of social developments in the 1980s, technology now seems to occupy this category. This gives rise to a critical and creative need for design. TA can make important contributions here with regard to the evaluation of these (new) socio-technical areas of action.
 
Literature:
• Aichholzer, G. (2016): Industry 4.0: Perspectives for work and employment. In: TAB letter No. 47, July 2016, pp. 29-33
• Hirsch-Kreinsen, H .; Ittermann, P .; Niehaus, N. (2015): Digitization of industrial work. The Industry 4.0 vision and its social challenges. Baden-Baden
• Huws, U .; Dahlmann, S .; Flecker, J .; Holtgrewe, U .; Schönauer, A .; Ramioul, M .; Geurts, K. (2009): Value chain restructuring in Europe in a global economy. Leuven
• Krings, B.-J. (2013): Work and Technology. In: Grunwald, A. (Ed.): Handbuch Technikethik. Stuttgart, pp. 217-222
• Offe, C., 1983: Work as a key social category? In: Matthes, J. (ed.): Crisis of the working society? Negotiations of the 21st German Sociological Congress in Bamberg 1982. Frankfurt a. M: Campus, pp. 38-65
• Rammert, W .; Schulz-Schäfer, I. (Ed.) (2002): Can machines act? Sociological contributions to the relationship between humans and technology. Frankfurt a.M./ New York
• Schwemmle, M .; Wedde, P. (2012): Digital work in Germany. Potentials and problem areas. Report published by the Friedrich Ebert Foundation, Bonn

 
Dr. Bettina-Johanna Krings, Social scientist, senior scientist at the Institute for Technology Assessment (ITAS) at (KIT), head of the research area: "Knowledge Society and Knowledge Policy" at ITAS. Thematic focus: Technical innovations and effects on work structures, concepts and methods of technology assessment, sociological theories of the modernization of societies. For many years she has been coordinating the topic: Work & Technology at ITAS.

Getting a Design Job in 2030 - New (digital) working environments in creative professions and requirements for future educational offers?

Getting a Design Job in 2030 - New (digital) working environments in creative professions and requirements for future educational offers?

Frank Heidmann, Anouk Meissner

Creative courses, especially the various traditional (e.g. communication, industrial and product design) and new design disciplines (e.g. interaction design, service design, social design) are still very popular worldwide. Their curricula, not only in Europe and North America - regardless of the differentiation between the courses - often still refer to the Bauhaus tradition and paradigms of the Ulm School of Design. They move in the area of ​​tension of a more traditional, holistic principle of design studies, which, with the expression design, suggests a holistic approach to aesthetic, form-giving practice and its integration into the design of living environments, as well as newer, more methodological courses. These focus on learning and applying participatory problem-solving methods and innovation processes and use digital tools more or less confidently or have the conception of digital services and smart objects as learning content. The latter developments are accompanied by an advance of design into new application domains and an expansion of design artifacts - away from physical products and towards complex services and systems in business and society. The phenomenon of the rise of "Design Thinking" as a creative and problem-solving method in companies and institutions is a remarkable accompaniment to this expansion of paradigms of the design process into society.
These - in and of themselves gratifying - developments in the design disciplines and design practice are, according to the basic thesis of this article, not yet sufficiently integrated into the curricula of the design courses and the newer fields of study such as interaction design have not yet found sufficiently future-proof answers to digitization . The fundamental question arises as to whether the skills, subject areas and soft skills taught today are robust enough to face ever faster development cycles in the various fields of technology and disruptive innovations in business, and whether it is sufficient to master user-centered methods, design thinking and agile processes to meet future demands. Finally, the question of the responsibility of designers and their role will be discussed at a time when political and social discussions are increasingly about emotions instead of facts and ever larger sections of the population are ignoring facts and willingly accepting lies.
All existing studies on future requirements for university graduates as the consequences of digitization and globalization come to a similar set of future work skills, which usually consist of the competence to solve complex problems (Complex Problem Solving) and the ability to be creative (Creative Intelligence, Novel & Adaptive Thinking). Additional skills address new knowledge of how to deal with large amounts of data (computational thinking) as well as interpersonal skills (social intelligence, people management, cross-cultural competency, etc.). This skill set is based on the massive introduction of intelligent machines (AI, machine learning), which lead to a redistribution of knowledge, know-how and economic opportunities. It is assumed that this will benefit above all those who know how to use intelligent machines and how to cooperate with one another. In relation to the design disciplines, these developments lead to the following three specific challenges, which are addressed in the article:
1) In the future, designers will be confronted with complex quantitative briefings more and more frequently. These are the result of the increasing spread of sensors, communication and computing power in everyday objects and environments (IoT), which unleash an unprecedented flood of data (big data). As a result of this development, the demand for skills to interact with data, to recognize patterns in data and to make data-based decisions will increase. Examples of the required »data literacy« in the design context are products and services in the areas of eHealth, self-optimization (quantified self), human-machine interfaces (HCI) for machines and robots, autonomous vehicles, smart homes, smart cities and IT security. The curricula in the design courses have so far offered little or no modules with specific learning content on data quality & data uncertainty, machine learning & data mining, statistical & quantitative analysis or (big) data ethics, geared towards the previous experience and special motivations of students in creative disciplines . The adoption of learning content produced for other disciplines, e.g. in the form of MOOCs, for the aboveTopics has not proven itself and fails to recognize the importance of motivational connectivity for learning and teaching processes in the respective specialist cultures.
2) In the future, designers will act less and less as designers of a specific product or service. This development began at the end of the 20th century with the establishment of participatory co-design methods, which give the future user of a product or service a much more active role in the design process. And it is accelerating through the digitization of production and logistics processes and the further development and the drop in prices of 3D printers and associated software solutions that will enable laypeople to produce more complex products independently in the future. The role of the designer is changing to that of a moderator and tutor or problem solver, who still needs her original design, creative skills as a central competence, but uses them in a significantly more complex process and in increasingly demanding transdisciplinary contexts of use and provides additional knowledge in Research methods, especially quantitative and qualitative social research and mixed methods, as well as economic and organizational sociological knowledge are required.
3) In the future, designers will have to learn new technological tools (software, sensors, manufacturing technologies, marketing tools, logistics processes, etc.) for their design and problem-solving activities in ever shorter periods of time and thus their "digital literacy" - understood as competence in dealing with a large one Expand the range of digital tools in different learning processes and as an indicator of the ability to critically evaluate web resources. There is a demand for new learning content and learning formats in the design courses, which convey the competence for quick familiarization and a high degree of flexibility and adaptability to new tools or for the evaluation of tools.
In summary, the article uses the example of creative professions to show how (digital) working environments will change in the next few years and how educational offers and curricula must adapt to these challenges.
 
Frank Heidmann, Prof. Dr., is professor for "Design of Software Interfaces" and head of the "Interaction Design Lab" at the Potsdam University of Applied Sciences. His areas of interest include the design and evaluation of human-technology interfaces, the visualization of spatial data (geovisualization), smart cities and green IT / sustainable design.
Anouk Meissner, M.A., is a research assistant at the design department at the Potsdam University of Applied Sciences. She is concerned with the strategic further development of the design courses (B.A./M.A.) And supervises the master’s program. Her research interests lie in method development and the use of methods in design research.

Time sovereignty between self-compulsion and assigned freedom in projects

Time sovereignty between self-compulsion and assigned freedom in projects

Sibylle Peters, Hans-Liudger Dienel, Ansgar Düben

As part of the research project "Working Time Sovereignty", the TU Berlin, the nexus Institute for Cooperation Management and Interdisciplinary Research and the SRH Fernhochschule - The Mobile University on behalf of the German Society for Project Management (GPM) had a total of 424 project employees in spring 2015 to organize their working hours themselves (Working time sovereignty).
The study shows that the density and complexity of the work in projects has increased and the delimitation of the perception of knowledge-intensive activities is taking place. As a result of the instructions from the organization to the project employees to organize their working hours in a self-organized manner, it becomes clear that fundamental questions regarding the regulation of working hours have not changed. The division and distribution of working time has been transferred to the personal responsibility of the actors through the authority to issue instructions. The work density (complexity) is thus changing. However, regulations on or about working hours have not explicitly changed, but have been shifted to the personal responsibility of the actors. Working hours are therefore not adapted to the conditions of knowledge work. In these change processes, the organization and its line management no longer perceive the inherent delegation and instruction powers themselves, which they are less and less able to do due to increasing complexity. You shift them to the project employees who are faced with complex tasks when it comes to the distribution of working hours.
The location, duration and volume in the distribution of working time take on various facets with regard to an effective and efficient design of and in projects, which must be viewed and evaluated in a differentiated manner. The spatial location of the working hours (variety of locations and change of location of the processing) increases the lack of transparency in projects, it favors a certain mistrust and strengthens a comparison and a sense of justice in the absence of the actors. Apparently there are only a few practicable instruments that allow the effects of attendance and absence on the work result to be compared.
From an individual perspective, autonomy over the position of working hours is an indispensable prerequisite for dividing autonomous working hours in a self-determined way so that the actors can differentiate their working hours; Intensive working hours (office) and assigned working hours for extended, additional work tasks. The duration of intensive activities and assigned activities cannot be avoided. This is granted to the project employees in the form of instruction rights and it becomes a fundamental element and prerequisite for working in the project. Some knowledge workers even hide extended work activities from the teams or colleagues or do them if the colleagues cannot see it. Others consciously let their colleagues participate in the organization of their working hours and thus also in their overtime as attendance.
The distribution of the volume of working time does not seem to be a daily problem for the project actors; you can take a break after the project is over. The freedom to organize working hours increases the pressure to do everything “well and right” from an individual perspective. Although the project actors do not perceive possible time burdens as a significant negative burden, the actual core of the work itself can no longer be grasped in working time regulations alone. Connected activities are part of the duration of the daily work. In this context, target agreements and trust-based work are possibly suitable instruments for assessing the self-organization of the work tasks to be carried out in unlimited working hours and providing remedial measures if the agreed target results are adhered to.
 
Authors::
Prof. Dr. Sibylle Peters, University of Magdeburg, Faculty of Education, focus on project and knowledge management, management development, now visiting scholar at the TU Berlin, Faculty of Labor and Education.
Prof. Dr. H.L.Dienel, TU Berlin, focus: work, technology, participation, mobility research.
Düben, A. MA