SoSe 21: Ethical Foundations of Data Science
Christoph Benzmüller
Additional information / Pre-requisites
The course is held in cooperation with Prof. Sabine Ammon and Jessica De Jesus de Pinho (TU Berlin).
Please register by email at tu.ethicsofai@gmail.com by 1 week before the start of the seminar, stating your subject background, semester, and motivation. Further information will be provided in the ISIS course (https://isis.tu-berlin.de/) at the beginning of the semester. Please check the course catalog or https://www.philtech.tu-berlin.de/menue/studium_und_lehre/Â for up-to-date information on the course. The seminar will be held via Zoom.
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The seminar takes an innovative and experimental approach to ethics with an interdisciplinary focus enabled by collaboration between the Computer Science, Engineering Science and Philosophy of Technology departments.
It involves engaging with the theoretical and practical approaches that address the intersection of ethics and technology, in this case AI. Students will learn to critically assess the relationship between technology and society and to analyze the interactions between technology and society from an ethical perspective. Furthermore, students will deal with the deconstruction of the concept of neutrality of technology and learn to critically assess it. At the same time, the environment will be taken as a stakeholder in its own right in order to consider the impact of technological applications from a sustainability perspective. The module will provide students with the necessary theoretical foundations stemming from both computer science (AI and digital technologies) and ethics. This knowledge will be put into practice and deepened through case-based projects carried out in interdisciplinary groups. The projects will address the current challenges encountered through the use of AI technologies in different fields of application (e.g., medical, financial, social etc.), as well as discuss different implementations and possible avenues of research that could enable the development of ethically acceptable AI systems. Students will prepare a presentation of their project as well as a scientific poster. The seminar is held in cooperation with the Data Science program of FU Berlin.
Further contributors:
- Jürgen Altmann (Dortmund): Expert reporting on the current state of military AI applications (confirmed)
- Selected further experts (e.g. from UNA Europa Network or from the emerging Berlin Ethics Lab)
Target group: Master’s students (Bachelor’s students from the fifth semester onwards)
Prerequisites: None
Block course format: The main part pf the seminar will be held in the form of a two week intensive block course (9:00-12:00 and 13:30--15:00) to be held from 14.-25. September. A mixture of lectures (including invited lectures by experts), student presentations and student projects/group work will be offered. The seminar includes an intensive preparation and postprocessing phase.
Student deliverables: Presentation (30 + 15 min), report (about 10 pages), further contributions depending on selected topic (surveys, programming, modeling, empirical studies), active participation in discussions
Description: Ethical and legal challenges in AI and Data science will be identified and options to resolve or control them will explored and discussed. The list of topics that will be addressed include:
- Introduction, survey and delineation on core notions, including "AI", "Data Science", "Machine Learning", "Ethics and Law in AI and Data Science"
- Survey and discussion of international positions and recommendations on ethical and legal regulation of AI and data science applications
- Clarification and discussion of relevant notions, including "Trustworthy AI", "Explainable AI", "Transparent AI"
- Elaboration of a spectrum of critical and non-critical applications of AI and data science technology
- Exemplary discussion of selected, critical application areas, including e.g. military applications, automated financial markets, criminal profiling, etc.
- Adversial attacks, and potential countermeasures
- Bias in data science and machine learning, and potential countermeasures
- Means to explain and assess decision making in data science and AI
- Means to enforce ethical and legal control in data science and machine learning
- Means to enforce ethical and legal control in large, integrated AI systems
- Ethics and security
14 Class schedule
Regular appointments