Melanie Kilian studied media science and business administration ath the University of Regensburg. Afterwards she studied general and comparative media studies, also at the University of Regensburg. The subject of her master thesis was: “The anatomy of the network. Or: Why big data is inevitable.”
Melanie Kilian has been a research assistant and lecturer at the Chair of Information Science at the University of Regensburg since 2016 and has also been a doctoral student there since 2018.
Her teaching takes place in the areas of information philosophy, interview analysis using grounded theory, the project seminar Requirements – Engineering & Air Travel Information Behaviour and the seminar on information behavior.
I work at the Chair for Information Science at th Institute for Information and Media, Language and Culture (I: IMSK)
I offer Courses on information philosophy / ethics, on the theory and practice of qualitative research and on human information behavior. My events are aimed to information scientists and media IT specialists.
My courses in information philosophy and ethics deal with methods and applications in the fields of big data, artificial intelligence and machine learning from an (informaion) ethical and epistemological perspective. Concepts and in particular concrete use cases as well as cas studies that are related to the (intelligent) processing of (mass) data are systematically analyzed using techniques of ethical reasoning or questioning, ethical decision-making, the attribution of responsibility as well as questions about the autonomy of knowledge. All of these analyzes always focus not only on the philosophical penetration of the object, but above all on the development ofpractical recommendations with the relevant AI methods.
I do not teach AI methods or the like. I impart the (information) ethical or epistemological view of such methods or applications to the students by means of lecture units on general and ethical concepts, guided group discussions, case studies, in which the students work in teams to answer specific questions, educational games, reading units and – depending in the course and topic – accompanying relevant project or seminar work.
After successfully completing my course in the field of information philosophy and ethics, students are able to answer ethical questions and problems to reflect analytically in the context of software and data processing, to fall back on established general ethical concepts as well as to make own relevant ethical judgement or to give practical recommendations for action and ultimately from dealing with the topic of relevant software projects or empirical studies to counter or explore derive open ethical issues.
Methods and applications related to big data, artificial intelligence and machine learning are not only relevant in research, but have long since become part of our everyday life – when we use search engines, read news on the social web, online data, online – shopping, video games, using smart personal assistants or gadgets, AI and big data are (often) not far. Artificial Intelligence & Co. – depending on the state and industry – have long been part of the (standard) repertoire of companies or state institutions in credit and insurance, in the judiciary or in recruiting and in combating terrorism. So it should come as nor surprise that AI skills are becoming less and less perceived as mere specialist knowledge and are incresingly becoming a key qualification in the modern world of work.
I mainly deal with thick data instead of big data 😉 That means, I mainly do qualitative research.
Question not relevant to me – I have nothing to offer directly related.
Question not relevant to me – I do not teach AI methods and do not research on the topic; I know about it from (self-) study.
Question may not be relevant to me – I do not teach AI methods and do not research on the topic; However, I am not sure whether I understand the question correctly, an answer might be: In the area of information ethics, responsibility attributes in connection with applications based on machine learning are a great challenge, if not sometimes still a solution Task.
Have a look at question 6.
Question not relevant to me – I currently have nothing to offer
As already explained, AI competences are less and less perceived as mere specialist knowledge, but increasingly represent a key qualification in the modern world of work. Universities should therefore react to this development at an early stage by offering courses in the fields of big data, artificial intelligence and machine learning can also be completed on a part-time basis and the skills acquired can also be certified on the basis of (generally) recognized guideline – as guideline that is already known, for example, in connection with proof of qualifications for language skills.
As already explained, AI competences are less and less perceived as mere specialist knowledge, but increasingly represent a key qualification in the modern world of work. Universities should therefore react to this development at an early stage by offering courses in the fields of big data, artificial intelligence and machine learning can also be completed on a part-time basis and the skills acquired can also be certified on the basis of a (generally) recognized guideline – a guideline that is already known, for example, in connection with proof of qualifications for language skills.
Question not relevant to me – in my opinion everything was addressed
Ms. Kilian, thank you for taking the time to answer these questions. We wish you a nice day!