053620 VU Data Ethics and Legal Issues (2025S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 10.02.2025 09:00 bis Fr 21.02.2025 09:00
- Abmeldung bis Fr 14.03.2025 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
- Vanessa Hannesschläger
- Nikolaus Forgó
- Katja Mayer
- Florian Seitz
- Eugenia Stamboliev
- Malgorzata Wilinska
- Hande Özkayagan Prändl
Termine (iCal) - nächster Termin ist mit N markiert
Attention: The introductory session on 3 March will take place online!
The sessions from 5 May onwards will also take place online.
For more information on examination and dates, please see the course Moodle page.
-
Montag
03.03.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
10.03.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
17.03.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
24.03.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
31.03.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
07.04.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
N
Montag
28.04.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
05.05.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
12.05.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
19.05.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
26.05.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
02.06.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
16.06.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
23.06.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6 -
Montag
30.06.
13:15 - 16:30
Digital
Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
50% ethics focus: participation, group work, written assignment & exam
50% legal focus: exam
50% legal focus: exam
Mindestanforderungen und Beurteilungsmaßstab
There is no mandatory prerequisite for this class.The grading scale for the course will be:
1: at least 87.5%
2: at least 75.0%
3: at least 62.5%
4: at least 50.0%In order to pass the course successfully, you will need to reach a minimum of 30% on each of the assessments.
1: at least 87.5%
2: at least 75.0%
3: at least 62.5%
4: at least 50.0%In order to pass the course successfully, you will need to reach a minimum of 30% on each of the assessments.
Prüfungsstoff
* Ethical issues raised by AI and data science
* Societal challenges
* Legal Basics
* Data protection and intellectual property law
* Current legal developments, especially concerning the regulation of artificial intelligence
* DH tools for legal issues in practice
* DH research infrastructures
* Open Science
* Legal issues with source material
* Legal issues with research data
* Societal challenges
* Legal Basics
* Data protection and intellectual property law
* Current legal developments, especially concerning the regulation of artificial intelligence
* DH tools for legal issues in practice
* DH research infrastructures
* Open Science
* Legal issues with source material
* Legal issues with research data
Literatur
* Coeckelbergh, Mark. 2020. AI Ethics. MIT Press.
* Coeckelbergh, Mark. 2019. Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, https://link.springer.com/article/10.1007/s11948-019-00146-8
* Crawford, Kate. 2021. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press.
* Perez, Caroline Criado. 2019. Invisible Women: Data Bias in a World Designed for Men. New York: Abrams.
* Dignum, Virginia. 2020. “Responsibility and Artificial Intelligence.” In The Oxford Handbook of Ethics of AI, edited by Marcus Dubber, Frank Pasquale, and Sunit Das, 215–33. University of Oxford Press.
* Fuchs, Christian & Sevignani 2013 What is Digital Labour? https://www.triple-c.at/index.php/tripleC/article/view/461
* House of Commons 2018 report “Algorithms in Decision-Making https://publications.parliament.uk/pa/cm201719/cmselect/cmsctech/351/35104.htm
* Mittelstadt, Brent, et al. 2016. The ethics of algorithms: Mapping the Debate. Big Data & Society https://journals.sagepub.com/doi/full/10.1177/2053951716679679
* Zou, James & Schiebinger, Londa. AI can be sexist and racist - it’s time to make it fair. Nature https://www.nature.com/articles/d41586-018-05707-8
* Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; et al. (15 March 2016). "The FAIR Guiding Principles for scientific data management and stewardship". Scientific Data 3: 160018. doi:10.1038/sdata.2016.18. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175/
* DARIAH-EU. https://www.dariah.eu/
* DARIAH working group Ethics & Legality in Digital Arts & Humanities ELDAH. https://eldah.hypotheses.org/
* CLARIN ERIC. https://www.clarin.eu/
* CLARIN Legal and Ethical Issues Committee CLIC: Copyright Law Overview. https://www.clarin.eu/content/clic-overview-copyright-law
* CLARIN Legal and Ethical Issues Committee CLIC: Introduction to Copyright and Related Rights. Orphan works. https://www.clarin.eu/content/clic-orphan-works
* Vanessa Hannesschläger. Common Creativity international. CC-licensing and other options for TEI-based digital editions in an international context. In Journal of the Text Encoding Initiative, Issue 11 (2016 Conference Issue), July 2019 -, Online since 17 November 2019. DOI: https://doi.org/10.4000/jtei.2610
* DARIAH ELDAH Consent Form Wizard (CFW). https://consent.dariah.eu/
* Bates, Jo, Yu-Wei Lin, and Paula Goodale. 2016. ‘Data Journeys: Capturing the Socio-Material Constitution of Data Objects and Flows’. Big Data & Society 3(2):205395171665450. doi: 10.1177/2053951716654502.
* Kitchin, Rob. 2014. ‘Big Data, New Epistemologies and Paradigm Shifts’. Big Data & Society 1(1):205395171452848. doi: 10.1177/2053951714528481.
* Olteanu, Alexandra, Carlos Castillo, Fernando Diaz, and Emre Kıcıman. 2019. ‘Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries’. Frontiers in Big Data 2:13. doi: 10.3389/fdata.2019.00013.
* European IPR Helpdesk, Copyright Essentials (2022). https://op.europa.eu/en/publication-detail/-/publication/8ca54353-87f9-11ec-8c40-01aa75ed71a1/language-en/format-PDF/source-251139731
* Kohl, U., & Charlesworth, A. (2016). Information Technology Law https://doi-org.uaccess.univie.ac.at/10.4324/9780203798522
* EU, Handbook on European data protection law (2018) https://op.europa.eu/en/publication-detail/-/publication/5b0cfa83-63f3-11e8-ab9c-01aa75ed71a1 (Sections 2, 3, 4, 6.1, 9.4, 10.1)
*Rosati, Eleonora. 2019. ‘Copyright as an Obstacle or an Enabler? A European Perspective on Text and Data Mining and its Role in the Development of AI Creativity’. Asia Pacific Law Review 27(2):198-217. doi: 10.1080/10192557.2019.1705525.
* Joler, V. & Crawford, K. (2018) Anatomy of an AI System: https://anatomyof.ai/
* Joler, V. & Crawford, K. (2023) Calculating Empires: https://calculatingempires.net/
* Coeckelbergh, Mark. 2019. Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, https://link.springer.com/article/10.1007/s11948-019-00146-8
* Crawford, Kate. 2021. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press.
* Perez, Caroline Criado. 2019. Invisible Women: Data Bias in a World Designed for Men. New York: Abrams.
* Dignum, Virginia. 2020. “Responsibility and Artificial Intelligence.” In The Oxford Handbook of Ethics of AI, edited by Marcus Dubber, Frank Pasquale, and Sunit Das, 215–33. University of Oxford Press.
* Fuchs, Christian & Sevignani 2013 What is Digital Labour? https://www.triple-c.at/index.php/tripleC/article/view/461
* House of Commons 2018 report “Algorithms in Decision-Making https://publications.parliament.uk/pa/cm201719/cmselect/cmsctech/351/35104.htm
* Mittelstadt, Brent, et al. 2016. The ethics of algorithms: Mapping the Debate. Big Data & Society https://journals.sagepub.com/doi/full/10.1177/2053951716679679
* Zou, James & Schiebinger, Londa. AI can be sexist and racist - it’s time to make it fair. Nature https://www.nature.com/articles/d41586-018-05707-8
* Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; et al. (15 March 2016). "The FAIR Guiding Principles for scientific data management and stewardship". Scientific Data 3: 160018. doi:10.1038/sdata.2016.18. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175/
* DARIAH-EU. https://www.dariah.eu/
* DARIAH working group Ethics & Legality in Digital Arts & Humanities ELDAH. https://eldah.hypotheses.org/
* CLARIN ERIC. https://www.clarin.eu/
* CLARIN Legal and Ethical Issues Committee CLIC: Copyright Law Overview. https://www.clarin.eu/content/clic-overview-copyright-law
* CLARIN Legal and Ethical Issues Committee CLIC: Introduction to Copyright and Related Rights. Orphan works. https://www.clarin.eu/content/clic-orphan-works
* Vanessa Hannesschläger. Common Creativity international. CC-licensing and other options for TEI-based digital editions in an international context. In Journal of the Text Encoding Initiative, Issue 11 (2016 Conference Issue), July 2019 -, Online since 17 November 2019. DOI: https://doi.org/10.4000/jtei.2610
* DARIAH ELDAH Consent Form Wizard (CFW). https://consent.dariah.eu/
* Bates, Jo, Yu-Wei Lin, and Paula Goodale. 2016. ‘Data Journeys: Capturing the Socio-Material Constitution of Data Objects and Flows’. Big Data & Society 3(2):205395171665450. doi: 10.1177/2053951716654502.
* Kitchin, Rob. 2014. ‘Big Data, New Epistemologies and Paradigm Shifts’. Big Data & Society 1(1):205395171452848. doi: 10.1177/2053951714528481.
* Olteanu, Alexandra, Carlos Castillo, Fernando Diaz, and Emre Kıcıman. 2019. ‘Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries’. Frontiers in Big Data 2:13. doi: 10.3389/fdata.2019.00013.
* European IPR Helpdesk, Copyright Essentials (2022). https://op.europa.eu/en/publication-detail/-/publication/8ca54353-87f9-11ec-8c40-01aa75ed71a1/language-en/format-PDF/source-251139731
* Kohl, U., & Charlesworth, A. (2016). Information Technology Law https://doi-org.uaccess.univie.ac.at/10.4324/9780203798522
* EU, Handbook on European data protection law (2018) https://op.europa.eu/en/publication-detail/-/publication/5b0cfa83-63f3-11e8-ab9c-01aa75ed71a1 (Sections 2, 3, 4, 6.1, 9.4, 10.1)
*Rosati, Eleonora. 2019. ‘Copyright as an Obstacle or an Enabler? A European Perspective on Text and Data Mining and its Role in the Development of AI Creativity’. Asia Pacific Law Review 27(2):198-217. doi: 10.1080/10192557.2019.1705525.
* Joler, V. & Crawford, K. (2018) Anatomy of an AI System: https://anatomyof.ai/
* Joler, V. & Crawford, K. (2023) Calculating Empires: https://calculatingempires.net/
Zuordnung im Vorlesungsverzeichnis
Modul: DEL
Letzte Änderung: Do 27.02.2025 13:05
The first part will cover a first introduction to ethical issues around data and technology by means of lectures with discussion:
* Introduction to AI, AI ethics and algorithm ethics
* Introduction to ethics of AI & data science + narratives about AI
* Privacy and digital labor + future of work
* Responsibility and explainability + Bias/fairness
* Climate and environment: Opportunities and ethical problemsThe second part will bridge to the more practical/empirical and socio-political aspects and include the following topics:
* Introduction to Critical Data and Infrastructure Studies: We will explore key concepts and approaches to understand how data practices shape and are shaped by socio-technical power.
* Calculating Empires: Using the Anatomy of AI and Calculating Empires platforms, we will engage in an interactive exercise to critically examine topics like the socio-materiality of data, data colonialism or global AI governance. Groups will present and discuss their outcomes in the seminar.
* Managing Data Responsibly: Lessons from Data Ethics, Open Science, and Digital Humanism: This unit focuses on practical frameworks for responsible data management, drawing insights from data ethics, open science principles, and digital humanism to promote ethical and sustainable data practices.Block II (legal focus):
The third part of the course introduces students to legal thinking and will cover the following scope of legal issues in the digital realm:
* Introduction into the legal system in Europe and Austria / legal resources
* Introduction to the fundamental rights closely related to data and data ethics
* Introduction to European data protection law
* Introduction to the regulation of artificial intelligence in the EU, specifically Artificial Intelligence Act
* Introduction to intellectual property law, in particular copyright, licenses, and text and data mining exceptionThe fourth part of the course moves into practical questions and will be building on the introduction to legal basics outlined above. The course will provide a detailed overview of the most commonly encountered legal issues around technological and data related aspects in digital humanities / research projects. This includes
* Source material and copyright (copyright, licensing and Open Science, orphan works and more)
* Data privacy of research subjects (GDPR, living research subjects, deceased research subjects, other invovled persons)
In addition, the course will introduce a number of tools developed and infrastructure maintained by the DH community to tackle these issues. Students will learn about the most important research infrastructures in the field of DH (CLARIN, DARIAH) and their working groups on legal and ethical issues (CLIC, ELDAH).
Parts three and four of the course relating to legal issues will be assessed by an exam taking place on Moodle.