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In IT security today, the usage of AI is already established in multiple domains. SPAM detection is a well-known example where support vector machines try to distinguish wanted from unwanted emails. Author attribution combines natural language forensics and machine learning. Deep learning helps in identifying illicit images and has improved malware detection as well as network intrusion detection. ...
The thought of using Artificial Intelligence (AI) and algorithmic decisionmaking (ADM) processes in our daily lives makes many of us feel insecure. Most consumers see more risks than opportunities, an attitude brought about by the black-box nature of algorithms and AI. When an organisation or public authority makes a decision supported by an algorithm, one can feel that one is at the algorithm’s mercy, finding it incomprehensible. Widespread consumer distrust of AI and ADM processes will make it difficult to improve their societal acceptance and therefore make it challenging to apply them in the business sector and in policy-making. Without trust on the consumer side, there can be no progress.
Artificial Intelligence (AI) will be one of the key technologies driving the future competitiveness of numerous industries. However, the term "AI" is defined in a variety of ways. AI could be understood as an umbrella term for technologies and systems that carry out tasks otherwise only executable with human intelligence. This requires specific skills that fall into the broad categories of "Sense", "Comprehend", "Act" and "Learn". Through machine learning, modern AI systems can be trained to adapt to changes in their environment, self-optimise and hence achieve better results than earlier versions of AI systems that were based on clearly defined, pre-programmed rules. Based on AI methods, rational and autonomous agents can be developed that collect and analyse relevant information from their environments, come to optimal conclusions based on certain performance parameters and eventually perform physical actions (e.g. robotics) or virtual actions (e.g. chat bots). Machine learning algorithms ensure that the information base of the system is continuously updated so that performance of the system is optimised in an iterative process.
Artificial intelligence (AI)1, together with big data, is the driving force behind the ever-accelerating digital revolution. AI has what it takes to call into question our fundamental concepts and processes of political, social, economic etc. order (Macron, 2018; Zuboff, 2018), and the law will not be spared. Therefore, all societal actors (inter alia from politics, the economy, legal practice and academia) must take responsibility for the crucial twin tasks of determining the right, balanced relationship between AI and the law, and even to hybridise them. ...
According to a survey by the Institute for Management and Economic Research (manager seminars, September 2018), 41% or almost half of those respondents over 60, considered it unlikely that they would be affected by Artificial Intelligence (AI) in the workplace. On the other hand, younger respondents more realistically estimated that significant AI-related changes would occur in their workplace within the next five years, not only in production and data analysis, but also in customer service and office practices across the board. ...
Policy research deals with the policy cycle encompassing problem definition, and policy development, implementation, enforcement and evaluation for different policy domains. On the one hand, this research field empirically describes and analyses how the phases of the policy cycle are processed by relevant social actors in interaction with industry, the media and civil society. On the other hand, policy research is concerned with the issue itself, i.e. the reasons for success and failure of policies that have or will run through the process. Here, the research field offers scientific policy advice including exante evaluation and assessments of potential futures, options, developments, and scenarios for policy domains to inform political debates and decisions. It is this latter function AI already has and will further influence policy research. ...
In April 2018 the European Commission announced its holistic approach to Artificial Intelligence (AI) based on the following three pillars: first, to boost financial support and encourage uptake by the public and private sectors in order to reach investments in AI-related research and innovation by at least 20 billion Euros by the end of 2020. The second pillar aims at preparing for socio-economic changes in terms of the upcoming transformation of the labour market. Finally, the European Commission will ensure an appropriate ethical and legal framework by developing AI ethics guidelines and providing guidance on the interpretation of the Product Liability directive. ...
The author, a professor of English linguistics at Freiburg University, was a member of the German Council of Science and Humanities (Wissenschaftsrat) from 2006 to 2012 and, in this capacity, was involved in this advisory body’s rating and assessment activities. The present contribution focusses on issues arising in the rating of research output in the humanities and is informed by his dual perspective, as planner and organizer of the ratings undertaken by the Wissenschaftsrat and as a rated scholar in his own discipline, English and American Studies.
Editorial : economic competence and financial literacy of young adults – status and challenges
(2016)
In modern society, the ability to deal with financial and economic matters is becoming increasingly important. This is true for both professionals – e.g., in the investment and banking sectors – and for individuals responsible for managing their financial and economic affairs in everyday life (Aprea et al., in press). This ability is generally described as economic competence, economic literacy or financial literacy. Despite the importance of these constructs, there is still a lack of clarity regarding the exact definitions, and specifically, which components they cover in detail. Furthermore, the terms economic competence and financial literacy are only loosely coupled. Economic competence is usually considered to be more comprehensive than financial literacy. However, recent research on financial literacy has followed a broader approach as well. ...
The necessity for well-founded teacher education in economics – findings from curriculum analyses
(2016)
Everybody has to make daily decisions requiring a good understanding of political and economic systems to manage and design our life but also to react on changes in these systems. Already in the early stages of adulthood, individuals need to decide on which job to choose, which party to vote for or on what money to spend on. For all these activities economical knowledge is necessary, which usually derives from economic education taught in schools in several subjects. ...