Responsible Data Science (Sommersemester 2023)

Lecturer: Dr. Simon David Hirsbrunner

General Information

• Weekly Hours: 2
• Credits: 3
• Graded: yes
• Enrolment Deadline: 01.04.2023 - 07.05.2023
• Examination time §9 (4) BAMA-O: 26.04.2023
• Teaching Form: Block seminar
• Enrolment Type: Compulsory Module
• Course Language: English
• Maximum number of participants: 20

Programs, Module Groups & Modules

Description

Please check the Github-website of the seminar for up-to-date information and links to resources: https://github.com/simonsimson/responsible-data-science.

The compact seminar provides students with strategies, knowledge and tools to reflect and address societal and ethical risks in data-intensive projects (data engineering, machine learning, etc.).

On the one hand, various ethical challenges and problem areas are introduced (e.g. privacy and informational self-determination, discrimination and fairness, transparency and explainability, reliability and security) and examples are discussed by means of practical use cases and interdisciplinary literature. On the other hand, students will learn about approaches and tools that can be used to address corresponding problems in the context of project development (scenario-based design, value-sensitive design, integrated technology development).
The seminar invites applicants from multiple scientific disciplines to facilitate a rich discussion of diverse perspectives and different value concepts within in the seminar.

Requirements

Affinity to interdisciplinary perspectives (computer science, design, social science, ethics, law), willingness to read scientific texts from different disciplines, ideally existing experience in the implementation of technology projects.

Literature

Literature from applied ethics research, social sciences (critical algorithm and data studies), computer science (data science, human-computer interaction), law and standards (EU AI regulatory draft, AI standards), politics (UN, OECD, German government) and civil society
(NGOs, journalism).

Learning

Block seminar each with input lectures by the instructor as well as by guests; short presentations by students based on the literature provided; joint exercises and discussions based on practical examples; development of analyses of ethical risks and addressing strategies for different scenarios of AI systems development and deployment.
Each session of the seminar will focus on one specific ethical issue area and on concrete strategies and tools for addressing corresponding risks.

Examination

The grade is based on a group presentation of 20 min incl. handout (40% of the grade) and a written scenario for a responsible development and operationalization of an AI system à 6 pages per person (60% of the grade). The group presentations (2-3 students) will each address one specific literature piece and related ethical issues. For the individual paper (written scenario), the lecturer will provide suitable topic areas from which the students can choose one. The corresponding topics will include fields of application of AI with high ethical risks at stake (e.g. facial recognition, telecommunication surveillance in police investigations, AI-supported personnel recruitment, creation of digital avatars representing existing persons, various fields of application of generative AI, autonomous driving).
 

Dates

19.4., 15.15 – 16.45 h
Session 1: Introductory session with assignment of presentation topics

26.04., 15.15 – 18.30 h
Session 2: Applied ethics and Responsible Data Science as socio-technical challenges

27.04., 15.15 – 18.30 h
Session 3: discrimination, fairness and diversity

17.05., 15.15 – 19.30 h
Session 4: privacy and informational self-determination

24.05., 15.15 – 19.30 h
Session 5: data quality, reliability and safety

25.05., 15.15 – 19.30 h
Session 6: people and planet

The session of 31.05. is rescheduled to 24.05.

01.06., 15.15 – 19.30 h
Session 7: transparency and accountability

Room: L-1.06

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