Universität Wien

053620 VU Data Ethics and Legal Issues (2023S)

Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

** The introductory session on 6 March will take place online! **
Sessions will take place on site until (incl.) 8 May. From 15 May, sessions will take place online. Examination will take place online.

Monday 06.03. 13:15 - 16:30 Digital
Monday 20.03. 13:15 - 16:30 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
Monday 27.03. 13:15 - 16:30 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
Monday 17.04. 13:15 - 16:30 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
Monday 24.04. 13:15 - 16:30 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
Monday 08.05. 13:15 - 16:30 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8
Monday 15.05. 13:15 - 16:30 Digital
Monday 22.05. 13:15 - 16:30 Digital
Monday 05.06. 13:15 - 16:30 Digital
Monday 12.06. 13:15 - 16:30 Digital
Monday 19.06. 13:15 - 16:30 Digital
Monday 26.06. 13:15 - 16:30 Digital

Information

Aims, contents and method of the course

Students will be introduced to ethical and legal challenges when dealing with real data. Specifically, the topics of the course are structured into four parts, two on ethical issues and two on legal issues.

The first part will cover the following ethical issues by means of lectures with discussion:
* 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 problems

The second part will bridge to the more practical/empirical and political-social aspects and include the following topics:
* Critical Data and Algorithm Studies, how to reflect data practices, abrief introduction to Science and Technology Studies (STS)
* Everyday surveillance, human sensors
* Hands-on project: experimenting with data / ML: Training ML, data sets, open data (for DH Students, we can tailor this to specific interests)
* Presentation of project findings and discussion

The third part will cover legal issues on:
* Introduction into the legal system in Europe and Austria / legal resources
* Introduction to European data protection and data security law
* Introduction to intellectual property law, in particular copyright, licenses, and text and data mining
* Recent trends, in particular Digital Services Act and Artificial Intelligence Act

In the fourth part, we 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 in DH projects.
* Example case studies - legal issues with source material:
- Copyright on primary texts
- Copyright on images (works of art)
- Data privacy issues with photographs
- Data privacy issues with diaries & letters
- Orphan works
* Example case studies - legal issues with research data:
- Ownership of scans
- Ownership of raw data; ownership of processed data
- Copyright on (scholarly) editions
- Ownership of scans
- Ownership of research output (e.g. papers)
- Ownership of code
- Research data about living persons and data privacy
- Non-research data about living persons and data privacy

In addition, the course will introduce a number of tools developed and infrastructure maintained by the DH community to tackle these issues (e.g. License Selector, Consent Form Wizard). 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). Additionally, the relevance of the legal framework in which we conduct our research and its consequences for the implementation of Open Science approaches will be discussed.

Assessment and permitted materials

50% written essay
50% final examination

Minimum requirements and assessment criteria

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.

Examination topics

* 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

Reading list

* 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 & Schibinger, Londa. AI can be sexist and racist - it’s time to make it fair. Nature https://www.nature.com/articles/d41586-018-05707-8
* OANA: Vienna Principles. A Vision for Scholarly Communication, 2015/16. https://viennaprinciples.org/
* 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/
* Open Science Network Austria OANA. https://www.oana.at/
* 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.
* Ienca, Marcello, and Effy Vayena. 2020. ‘On the Responsible Use of Digital Data to Tackle the COVID-19 Pandemic’. Nature Medicine 26(4):463–64. doi: 10.1038/s41591-020-0832-5.
* 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
* Share Your Work, Creative Commons. https://creativecommons.org/share-your-work/
* 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)

Association in the course directory

Modul: DEL

Last modified: Th 11.05.2023 11:27