This class is designed to provide TU Wien (graduate and post-graduate) students with an advanced knowledge of contemporary debates about ethical AI. Moving beyond conventional STEM-driven approaches focused on bias and explicability, the course aims to familiarise students with disciplines such as philosophy, science and technology studies, political science, sociology, and education sciences.
Course structure:
The ten lectures revolve around seminal texts that introduce fresh takes on the topic of ethical AI. Each article provides insights into social factors such as: institutional proximity, economic dependencies, epistemic conflicts, values, and power. Ideally, each lecture lasts 1 hour 30 minutes, divided in two 45 minute modules. It comprises a reading seminar and the presentation of short student projects. The nature of the projects will vary according to the number of attendants. The final lecture summarises the results of the course, and introduce a theoretical proposal in the wake of my presentation at the DigHum Summer School of this year .
Contents and Schedule
Tue May 2; 2023 - Lesson 1
Main societal debates
(Russell, S., Dewey, D. & Tegmark, M. (2015). Research Priorities for Robust and Beneficial Artificial Intelligence. Ai Magazine. 36. 105-114; Awad, E., Dsouza, S., Bonnefon, J.-F., Shariff, A. & Rahwan, I. (2020) Crowdsourcing Moral
Machines. Communications of the ACM. 63(3): 48-55)
Thurs May 4, 2023 - Lesson 2
Ethics of Data, Ethics of Algorithms
(Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2);Floridi L. & Taddeo M. 2016. What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083): 20160360. doi:10.1098/rsta.2016.0360)
Tue May 9; 2023 - Lesson 3
Values and Organisations
(Jobin, A., Ienca, M. & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399; Birhane, A., Kalluri, P., Card, D., Agnew, W., Dotan, R., Bao, M. (2021). The Values Encoded in Machine Learning Research, arXiv:2106.15590 [cs.LG])
Wed May 10, 2023 - Lesson 4
Power Asymmetries and Conflicts
(Whittaker. W. (2021). The steep cost of capture, IX Interactions, XXVIII(6), pp. 50-55; Miceli, M., Posada, J. & Yang, T. (2022). Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power? Proc. ACM Hum.-Comput. Interact. 6, GROUP, Article 34.)
Thurs May 11, 2023 - Lesson 5
Humans in the Loop
(Denton E., Hanna A., Amironesei R., Smart A. & Nicole H. (2021). On the genealogy of machine learning datasets: A critical history of ImageNet. Big Data & Society, 8(2); Tubaro, P., Casilli, A. A., & Coville, M. (2020). The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence. Big Data & Society, 7(1))
Tue May 16; 2023 - Lesson 6
Data as labor
(Fuchs, C. (2016). Digital Labor and Imperialism. Monthly Review, 67(8): 14-24; ILO (International Labour Organization). 2021. World Employment and Social Outlook 2021: The role of digital labour platforms in transforming the world of work. Report)
Wed May 17, 2023 - Lesson 7
The materiality of AI
(Green AI. Communications of the ACM, 63(12): 54-63, DOI: 10.1145/3381831; Crawford K. & Joler V. 2018. ‘Anatomy of an AI System: The Amazon Echo As An Anatomical Map of Human Labor, Data and Planetary Resources.’ AI Now Institute and Share Lab, https://anatomyof.ai)
Tue May 23; 2023 - Lesson 8
End-to-End Ethical AI
(Casilli, A. A. (2023). ‘End-to-End’ Ethical Artificial Intelligence. Taking into account the social and natural environments of automation. ETUI working paper)
Wed May 24 and Thurs May 25: to be confirmed
