„the history of the pill“
The students will appreciate the incredibly massive ways in which the scientific physiological and chemical research of the early 20th century influences our lives today.
"Quantitative Methods of Technology Assessment"
After the course has been successfully completed, students can outline and discuss system-analytical and prospective methods of technology assessment.
"Fairness, bias and transparency of algorithms"
Basic knowledge and increase awareness of the problem to fairness, bias and transparency of algorithms.
“Critical Algorithm Studies”
Students should gain an understanding of political, ethical, cultural and social dimensions of algorithmic information systems. The focus hereby will be on the interplay of various algorithm definitions, processed data and social impacts and consequences.
„the history of the pill“
Life-paths and works of the early Austrian pioneers of sex hormone researchers Ludwig Haberlandt, Eugen Steinach and Walter Hohlweg. It will be discussed how this work enabled the success of Carl Djerassi and others to develop a contraceptive "pill".
"Quantitative Methods of Technology Assessment"
In science, methods must be objective, that means anyone following the same method must be led to the same result. In the technique (follow) estimation, however, the focus is less on the result, but more on the conditions under which the result is produced (emergence). Often the IF and not the THEN is problematized, especially if the results obtained do not meet the expectations. The methodically correct results (THEN) are then not sacrosanct. This means avoiding objectivist hope, but generating knowledge dependent on the prerequisite, which must be reflexively penetrated and interpreted. In focus Input / output analysis, scenario techniques and model-based simulation are discussed.
"Fairness, bias and transparency of algorithms"
Learning algorithms answer more and more questions of our daily lives: one is admitted to a certain university; one gets a salary increase or placement at the employment office, one gets a loan, etc. Such algorithms are considered attractive because they are efficient, and also considered to be fair. However, to benefit from such technologies requires an idea of what principles we want to use these systems for, and how we can examine and correct aberrations. The presentation will discuss basic considerations of fairness, bias and transparency of algorithms. Using examples from cancer research and behavioral recognition, it shows how data and the data collection process influence a learning algorithm, making decisions that affect our lives.
“Critical Algorithm Studies”
The lecture provides an overview of the interdisciplinary field of research known as “Critical Algorithm Studies”. Critical analysis of algorithms has to start with a definition of the term – what are algorithms? By means of examples of the very recent past – such as the echo chambers (“filter bubble”) in social media through algorithmic curated news feeds, inherent preconceptions in image recognition or feedback loops in social risk scoring exemplified by the AMS algorithm – covert effects and problematic areas of algorithms beyond a purely technical discussion are addressed. Furthermore, we will explore how values and prejudices might manifest themselves in algorithmic systems and which possibilities exist to promote the (re-)production of ethical algorithmic systems.
