Matthias Leese: Catalogue data in Autumn Semester 2022

Name	Prof. Dr. Matthias Leese
Field	Technology and Governance
Address	Technologie und Governance ETH Zürich, IFW D 29.2 Haldeneggsteig 4 8092 Zürich SWITZERLAND
Telephone	+41 44 632 75 33
E-mail	matthias.leese@gess.ethz.ch
URL	https://matthiasleese.com/
Department	Humanities, Social and Political Sciences
Relationship	Assistant Professor

Number	Title	ECTS	Hours	Lecturers
853-8002-00L	The Role of Technology in National and International Security Policy	3 credits	2G	O. Thränert, A. Dossi, S.‑C. Fischer, M. Leese, N. Masuhr
Abstract	The lecture provides an introduction to the role of security and military technologies in the formulation and implementation of national and international security policies. The focus is on challenges posed by new and developing technologies, the transformation of military capabilities, and the question of regulation.
Learning objective	Participants will gain an in-depth overview of the many ways in which technology is becoming part of security policies and practices, in both civilian and military contexts.
Content	Der erste Teil befasst sich mit den vielgestaltigen und komplexen Beziehungen zwischen Konzepten nationaler und internationaler Sicherheit, der Förderung von Forschung und Entwicklung, ökonomischen Aspekten von Technologie, und Aussenpolitik und Diplomatie. Der zweite Teil behandelt die Auswirkungen von neuen Technologien auf militärische Kapazitäten, strategische Optionen, und Militärdoktrinen in Krieg und Frieden. Der dritte Teil konzentriert sich auf regulatorische Herausforderungen, die aus der Implementierung und der globalen Weiterverbreitung von Technologie resultieren. Der letzte Teil schliesslich beschäftigt sich mit den Herausforderungen für den Staat im Umgang mit neuen und noch in der Entwicklung befindlicher Technologien, vorrangig in den sensiblen Bereich der Rüstungsbeschaffung und des nachrichtendienstlichen Einsatzes.
Literature	Literatur für die einzelnen Sitzungen wird auf Moodle bereitgestellt.
Prerequisites / Notice	The lecture is being supported by a website on Moodle. If you have any questions, please contact Oliver Roos, oliver.roos@sipo.gess.ethz.ch.
860-0026-00L	Data Practices Number of participants limited to 20. Priority for Science, Technology, and Policy MSc and PhD students.	3 credits	2S	M. Leese
Abstract	The aim of this course is to establish an understanding of data as embedded in social contexts. Studying data from a social scientific perspective is necessary to account for these influences and analyze the ways in which data practices shape the ways in which data allow us to see and modify the world.
Learning objective	At the end of the term ,students will be able to:  reflect concepts and theories of data practices and situate them within wider social science contexts  identify key actors, sites, and domain contexts of data practices  choose appropriate ways and methods to study data practices empirically
Content	The aim of this course is to establish an understanding of data as embedded in social contexts. Data do not exist independently of the ideas, instruments, contexts and rationales used to generate, process, and analyze them. They are not neutral representations of external realities, but they are imbued with political and economic interests, cultural norms and tacit assumptions. Studying data from a social scientific perspective, it is thus necessary to account for these influences and analyze the ways in which data practices shape the ways in which data allow us to see and modify the world.
Lecture notes	Course materials are provided on Moodle.
Prerequisites / Notice	The quality of your experience in this course depends on your preparation and active participation. Students will be expected to read the required literature, subject it to critical examination, and discuss it in class. You will be required to prepare a short preparation assignment for one or two sessions (depending on the overall number of students enrolled in the course), consisting of the preparation of three discussion questions for the session’s readings. Due to the consecutive building blocks that we address throughout the semester, in the best scenario you should not miss any of the sessions. In case you should be unable to attend the seminar, please inform the course organizer by e-mail in advance (mleese@ethz.ch).
876-0201-00L	Technology and Policy Analysis	8 credits	5G	T. Schmidt, E. Ash, F. M. Egli, R. Garrett, M. Leese, A. Rom, B. Steffen
Abstract	Technologies substantially affect the way we live and how our societies function. Technological change, i.e. the innovation and diffusion of new technologies, is a fundamental driver of economic growth but can also have detrimental side effects. This module introduces methods to assess technology-related policy alternatives and to analyse how policies affect technological changes and society.
Learning objective	Introduction: Participants understand (1) what ex ante and ex post policy impact analysis is, (2) in what forms and with what methods they can be undertaken, (3) why they are important for evidence-based policy-making. Analysis of Policy and Technology Options: Participants understand (1) how to perform policy analyses related to technology; (2) a policy problem and the rationale for policy intervention; (3) how to select appropriate impact categories and methods to address a policy problem through policy analysis; (4) how to assess policy alternatives, using various ex ante policy analysis methods; (5) and how to communicate the results of the analysis. Evaluation of Policy Outcomes: Participants understand (1) when and why policy outcomes can be evaluated based on observational or experimental methods, (2) basic methods for evaluating policy outcomes (e.g. causal inference methods and field experiments), (3) how to apply concepts and methods of policy outcome evaluation to specific cases of interest. Big Data Approaches to Policy Analysis: Participants understand (1) why "big data" techniques for making policy-relevant assessments and predictions are useful, and under what conditions, (2) key techniques in this area, such as procuring big datasets; pre-processing and dimension reduction of massive datasets for tractable computation; machine learning for predicting outcomes; interpreting machine learning model predictions to understand what is going on inside the black box; data visualization including interactive web apps.
Literature	Course materials can be found on Moodle.