Ethical Questions in the Context of Data Engineering and Machine Learning (Wintersemester 2022/2023)

Dozent: Dr. Thilo Hagendorff

Allgemeine Information

• Semesterwochenstunden: 2
• ECTS: 3
• Benotet: Ja
• Einschreibefrist: 01.10.2022 - 31.10.2022
• Prüfungszeitpunkt §9 (4) BAMA-O: 31.03.2023
• Lehrform: Blockseminar
• Belegungsart: Pflichtmodul
• Lehrsprache: Deutsch
• Maximale Teilnehmerzahl: 30

Studiengänge, Modulgruppen & Module

Beschreibung

Description

From the perspective of ethics, the compact seminar deals with various topics in the context of Data Engineering, Machine Learning, and the associated (ethical and social) ramifications. The seminar will focus on different fields, ranging from theories of computer and AI ethics to concrete topics such as values in technology, fairness in machine learning, natural language processing, recommender systems, etc. The seminar focuses less on abstract ethical theories from philosophy, but rather on current, genuinely interdisciplinary research fields and papers (e.g. from Critical Data Studies), which deal directly with the intersection of ethics and computer science.

Learning

The purpose of the seminar is to become familiar with issues and methods from the field of technology, AI, and information ethics and to be able to apply them to individual use cases.

Voraussetzungen

The provided references should be read before the start of the seminar so that the papers can be discussed during the seminar. The texts will be made available via Moodle.

Literatur

References

Johnson, Deborah G. (2017): Can Engineering Ethics Be Taught? In The Bridge 47 (1), pp. 59–64.

Chiodo, Maurice; Bursill-Hall, Piers (2018): Ethics In Mathematics Discussion Paper, pp. 1–22.

Brey, Philip (2010): Values in technology and disclosive computer ethics. In: Luciano Floridi (Hg.): The Cambridge Handbook of Information and Computer Ethics. Cambridge, Massachusetts: Cambridge University Press, pp. 41–58.

Birhane, Abeba; Kalluri, Pratyusha; Card, Dallas; Agnew, William; Dotan, Ravit; Bao, Michelle (2021): The Values Encoded in Machine Learning Research. In arXiv: 2106.15590v1, pp. 1–28.

Kitchin, Rob (2017): Thinking critically about and researching algorithms. In: Information, Communication & Society 20 (1), pp. 14–29.

Jobin, Anna; Ienca, Marcello; Vayena, Effy (2019): The global landscape of AI ethics guidelines. In Nature Machine Intelligence 1 (9), pp. 389–399.

Hagendorff, Thilo (2022): A Virtue-Based Framework to Support Putting AI Ethics into Practice. In Philosophy & Technology 35 (3), pp. 1–246.

Morley, Jessica; Floridi, Luciano; Kinsey, Libby; Elhalal, Anat (2020): From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices. In Science and Engineering Ethics 26, pp. 2141–2168.

Selbst, Andrew D.; boyd, danah; Friedler, Sorelle A.; Venkatasubramanian, Suresh; Vertesi, Janet (2018): Fairness and Abstraction in Sociotechnical Systems. In ACT Conference on Fairness, Accountability, and Transparency (FAT) 1 (1), pp. 1–17.

Milano, Silvia; Taddeo, Mariarosaria; Floridi, Luciano (2020): Recommender systems and their ethical challenges. In AI & SOCIETY - Journal of Knowledge, Culture and Communication 35 (4), pp. 957–967.

Blodgett, Su Lin; Barocas, Solon; Daumé III, Hal; Wallach, Hanna (2020): Language (Technology) is Power: A Critical Survey of “Bias” in NLP. In Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault (Eds.): Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 5454–5476.

Brundage, Miles; Avin, Shahar; Clark, Jack; Toner, Helen; Eckersley, Peter; Garfinkel, Ben et al. (2018): The Malicious Use of Artificial Intelligence. Forecasting, Prevention, and Mitigation, pp. 1–101.

Zuboff, Shoshana (2015): Big other: surveillance capitalism and the prospects of an information civilization. In: Journal of Information Technology 30, pp. 75–89.

Lern- und Lehrformen

Compact seminar; group discussions; presentations if desired.

Leistungserfassung

Grading is based on the quality of a term paper. The exact criteria according to which the paper will be graded will be discussed in the last session of the seminar.

Termine

time	February 2nd, 2023	time	February 3rd, 2023	February 4th, 2023	February 5th, 2023
		09:15 to 10:45	Brey – Values in Technology	Morley et al. – From What to How	Brundage et al. – The Malicious Use of AI
		11:00 to 12:30	Birhane et al. – Values in Machine Learning	Selbst et al. – Fairness in Sociotechnical Systems	Zuboff – Surveillance Capitalism
14:00 to 15:30	Introduction	13:30 to 15:00	Kitchin – Thinking Critically about Algorithms	Milano et al. – Recommender systems	Final discussions / seminar papers
15:45 to 17:15	Johnson – Can Engineering Ethics Be Taught?	15:15 to 16:45	Jobin et al. – AI Ethics Guidelines	Blodgett et al – Bias in NLP
17:30 to 19:00	Chiodo – Ethics in Mathematics	17:00 to 18:30	Hagendorff – AI Virtues

Room: H-E.51/52

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