IC4 – Introduction to Ethics in AI

Lecturer: Heike Felzmann
Fields: Ethics, AI


The last few years have seen an explosion of societal uses of AI technologies, but at the same time widespread public scepticism and fear about their use have emerged. In response to these concerns, a wide range of guidance documents for good practice in AI have been published by professional and societal actors recently. Both as researchers in AI and as consumers of AI it is helpful to understand ethical concepts and concerns associated with the use of AI and to be familiar with some of these guidance documents, in order to be able to reflect carefully on their ethical and social meaning and the balance of their benefits and risks and adapt one’s practices accordingly.

This course provides a general introduction to emergent ethical issues in the field of AI. It will be suitable for anyone with an interest in reflecting on how AI impacts on contemporary life and society. Over the four sessions of the course we will introduce and reflect on ideas and practical applications related to the following topics:

  • Understanding privacy, consent and transparency
  • Automated decision-making, algorithmic biases, autonomous artificial agents and accountability for decisions by artificial agents
  • Assistance, surveillance, persuasion, and human replacement
  • Responsible design and implementation, trustworthiness, and AI for good


The goal of the course is for participants to gain familiarity with core ethical concepts and concerns arising in the development and societal uses of AI, allowing participants to engage in a differentiated and informed manner with the societal debates on AI.


Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press. (on Google Books)

HLEG on AI (2019) Ethics Guidelines for Trustworthy AI, https://ec.europa.eu/futurium/en/ai-alliance-consultation 

Nissenbaum, H. (2019). Contextual Integrity Up and Down the Data Food Chain. Theoretical Inquiries in Law, 20(1), 221-256. http://www7.tau.ac.il/ojs/index.php/til/article/download/1614/1715 (ignore the abstract, which is much more obscure than the rest of the article! Contextual integrity is a useful theory of privacy.)

Zuboff, S. (2019) The Age of Surveillance Capitalism: The fight for a human future at the frontier of power. (Youtube interviews with Zuboff might be a good introduction.)


Heike Felzmann is a lecturer in Ethics in the School of History and Philosophy at NUI Galway, Ireland. She works on ethics in information technologies (especially on healthcare robots and AI), research ethics, and general health care ethics. She has been part of several European projects, including H2020 MARIO on a care robot for patients with dementia, H2020 ROCSAFE on robot supported incident response, COST 16116 on robotic exoskeletons, COST RANCARE on rationing in nursing care, ITN DISTINCT on technology use in dementia care, ERASMUS PROSPERO on education on social robots for social care, and was the chair of the COST Action CHIPME on innovations in genomics for health. She has also had extensive experience with research ethics governance and research ethics training. She teaches ethics widely across disciplines and is looking forward to meeting the interdisciplinary audience at the IK.

Website: http://www.nuigalway.ie/our-research/people/humanities/heikefelzmann/

Affiliation: NUI Galway

IC3 – Introduction to Neuroscience

Lecturer: Till Bockemühl & Ronald Sladky
Fields: neurobiology, neuroscience, cognitive science


The brain, the cause of – and solution to – all of life’s problems. According to our brains it is the most fascinating structure in the known universe. Consisting of about 86 billion neurons of which each can form thousands of connections to other neurons it is also the most complex structure in the known universe. In this course we would like to give you a rough guide and introduction to the basic principles, fundamental theories, and methods of neuroscience.

We will demonstrate that neuroscience can be seen as a multi-modal, multi-level, multi-disciplinary research framework that aims at addressing the challenges of this megalomaniac scientific endeavor. We will see that different frameworks and methods can lead to conflicting empirical evidence, theoretical assumptions, and heated debates. However, we argue that this might be the only way to uncover the mysteries of our brain.

In this course we will cover a variety of scopes and perspectives. We will teach some of the fundamentals of neuroscience in human and non-human animals, but we will also explore some explanatory gaps between the different levels of inference.

On a phenomenal level we will investigate the functions of individual neurons and small networks. We will discuss if and how we can learn from (genetically modified) model animals about neural functions. To what degree is this relevant for understanding human brain function, such as learning and decision making? On the other hand, we will also investigate the state of the art in human brain mapping and cognitive neuroscience. Can findings from neuroimaging tell us anything at all about neurobiology – or are they just fancy illustrations that are better suited for children’s books?


  • To understand the anatomy and function of neurons
  • To understand the interaction of neurons in a functional network
  • To understand central methods and theories used in neurobiology and human cognitive neuroscience
  • To understand the scope of different methods and theoretical frameworks


  • Cacioppo JT, Berntson GG, Lorig TS, Norris CJ, Rickett E, Nusbaum H. Just because you’re imaging the brain doesn’t mean you can stop using your head: a primer and set of first principles. J Pers Soc Psychol. 2003 Oct;85(4):650-61. [Link]
  • Park HJ, Friston K. Structural and Functional Brain Networks: From Connections to Cognition. Science, 2013 Nov; 6158(342):1238411 [Link]
  • Bear MF, Connors BW, Paradiso MA. Neuroscience: Exploring the Brain. Wolters Kluwer Health. 2015.
  • Kandel, ER, Schwartz, JH, Jessell, TM, Siegelbaum, S, Hudspeth, AJ, & Mack, S (2013). Principles of Neural Science.


Till Bockemühl studied biology and philosophy at Bielefeld University. He did his diploma thesis as well as his doctoral thesis with Volker Dürr in the lab of Holk Cruse at Bielefeld University. Currently, he is a postdoctoral researcher in the lab of Ansgar Büschges at the University of Cologne. His main research interests comprise the motor control of locomotion, neuroethology, and computational neurobiology. To investigate these topics, he uses the fruit fly Drosophila and the ever-expanding toolkit of methodological opportunities this model organism has to offer.

Affiliation: University of Cologne
Website: http://www.zoologie.uni-koeln.de/bueschges-staff-tillbockemuehl.html

Ronald Sladky. My research focuses on the amygdala and emotion processing in the human brain. In addition, I am always working on new neuroimaging, data processing, and modeling methods. One of these new methods is real-time functional MRI, where people can learn to regulate their own brain states while they are inside the MRI scanner. This method is not only a promising therapeutic tool, it will also allow for completely new ways of discovering how our brains work.

Affiliation: University of Vienna
Website: http://sweetneuron.at

IC2 – Introduction to Psychology

Lecturer: Katharina Krämer
Fields: Psychology / Developmental Psychology, Social Psychology, and Clinical Psychology


This course is intended for all participants (psychologists and non-psychologists alike), who are curious about the human mind and its functions. And that is basically what psychology is about: Psychological research investigates the role of mental functions in individual and social behaviour and explores the physiological and biological processes that underlie cognitive functions and behaviours. As a social science, psychology aims to understand individuals and groups by establishing general principles and researching specific cases by using quantitative and qualitative research methods.

During this introduction to psychology we will get to know the major schools of thought, including behaviourism and cognitive psychology as well as psychoanalysis and psychodynamic psychology. Psychological research encompasses many subfields and includes different approaches to the study of mental processes and behaviour. In this course, we will focus in particular on the sub-disciplines of developmental psychology, social psychology, and clinical psychology. Thereby, we will explore the following questions:

  • How does the human mind develop through the live span? How do people come to perceive, understand, and act within the world? How do these processes change as people age?
  • How do humans think about each other? How do they relate to each other? What are the influences of others on an individual’s behaviour? How do people form beliefs, attitudes, and stereotypes about other people?
  • How and why do mental disorders develop? How can we prevent mental disorders and psychologically based distress? How can we promote subjective well-being and personal development?


  • To get an overview of the different sub-disciplines of psychology and psychological research methods
  • To get a broad idea how the human mind works, how people function and what motivates and explains their behaviour
  • To understand how psychological knowledge can be applied to the assessment and treatment of mental health problems


As there are many excellent textbooks on psychology and its sub-disciplines, this is just a small selection if you want to do some background reading.

  • Aronson, E., Wilson, T.D. & Akert, R.M. (2013). Social Psychology (8th Edition). Pearson.
  • Barlow, D.H. (2014). The Oxford Handbook of Clinical Psychology. Oxford University Press.
  • Gerrig, R.J., Zimbardo, P., Svartdal, F., Brennan, T., Donaldson, R. & Archer, T. (2012). Psychology and Life (19th Edition). Pearson.
  • Slater, A. & Bremner, J.G. (2017). An Introduction to Developmental Psychology (3rd Edition). The British Psychological Society and Wiley.


Dr. Katharina Krämer
Dr. Katharina Krämer

Katharina Krämer is a psychologist and psychoanalytic psychotherapist. She works as a professor for psychology at the Rheinische Fachhochschule Köln, Germany, and as a psychotherapist at the Department of Psychiatry at the University Hospital Cologne, Germany. In 2014, Katharina Krämer received her doctoral degree from the University of Cologne, Germany, on a thesis investigating the perception of dynamic nonverbal cues in cross-cultural psychology and high-functioning autism. She works with patients with different mental disorders, focusing on adult patients with autism. Her research interests include the application of Mentaliszation-Based Group-Therapy with patients with autism and the vocational integration of patients with autism.

Affiliation: Rheinische Fachhochschule Köln
Website: https://www.rfh-koeln.de/studium/studiengaenge/wirtschaft-recht/wirtschaftspsychologie/dozenten/katharina_kraemer/index_ger.html

IC1 – Introduction to Machine Learning

Lecturer: Benjamin Paassen
Fields: Artificial Intelligence / Machine Learning


This course is intended for non-machine learners with little to no prior knowledge. It will provide many examples as well as accompanying exercises and limit the number of formulae to a bare minimum, while instead maximizing the number of meaningful images. In more detail, the course will cover the following topics.

  • Session 1: Basics of optimization (What is a mathematical optimization problem? How do we model the world in optimization? How do we solve optimization problems?), basics of probability theory (What are distributions, joint and conditional probabilities, and Bayes’ rule? How do we maximize probabilities?), and linear regression from a geometric and a risk minimization perspective
  • Session 2: Machine learning from the perspective of distances for classification (how do I put things into known categories?), clustering (how do I discover new categories of things?), regression (how do I infer an unknown variable from a known one, based on examples?), and dimensionality reduction (how do I simplify data that is too big to process?)
  • Session 3: Neural-network-based learning (What is an artificial neural network? What are popular components? What kind of models can I build? How do I learn such models?) and the problems of generalization (When can learning fail? How do I prevent that? How can hackers attack my model?)
  • Session 4: Reinforcement learning (What is it and how do I do it?) and algorithmic fairness (What does it mean to be fair and what role to risk, reward, and curiosity play?)


  • becoming familiar with key concepts from machine learning (e.g. risk minimization, exploration versus exploitation, priors and posteriors, generalization)
  • achieving a high-level understanding of how the most popular machine learning methods work and which method can be used for which application (e.g. when not to use deep learning methods)
  • de-mystifying machine learning (it is just a collection of methods with certain assumptions)
  • optionally, becoming able to apply some machine learning methods in Python on your own data (exercises)



Benjamin Paassen
Dr. Benjamin Paassen

Benjamin Paassen received their doctoral degree in 2019 from Bielefeld University, Germany on the topic of ‘Metric Learning for Structured Data’. Prior work has focused on machine learning algorithms to support applications in computer science education and hand prosthesis research, but has also included research on discrimination in video game culture and in machine learning. Research interests include interpretable machine learning, metric learning, transfer learning, and fairness.

Affiliation: Bielefeld University and University of Sydney
Website: https://bpaassen.gitlab.io/