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| Detailed information |
| Original study plan |
Bachelor's programme Artificial Intelligence 2022W |
| Objectives |
Upon successful completion of this course, students will be able to: 1. define and sketch focal points of the main theories in feminist philosophy of science and feminist data studies; 2. identify different ethical perspectives in how algorithmic systems are designed and the impact they have for minorities and marginalized groups; 3. compare different ethical perspectives and imagine how they can be incorporated in different designs of AI tools
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| Subject |
This course explores how values shape science and engineering, with a special emphasis on issues related to gender and racial biases. In particular, we will focus on algorithmic systems and the biases they incorporate as a result of poor design. In order to grasp how algorithmic systems can perpetuate patterns of gender and racial injustice, it is critical to grasp how values and prejudices are sneaked, often inadvertently, in the design of AI tools. We will shed light on these issues by discussing works in feminist philosophy of science, and more recent feminist data studies. After understanding how algorithmic systems can be ethically controversial, we will discuss opportunities for a fair, just, and more inclusive ethical design.
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| Criteria for evaluation |
attendance and participation; group project; written exam of the content of the course
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| Methods |
discussions of texts; debates; group discussions
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| Language |
English |
| Study material |
- Fricker, Miranda. Epistemic Injustice: Power and the Ethics of Knowing. New York: Oxford University Press, 2007. (excerpts)
- Longino, Helen E. Science as Social Knowledge: Values and Objectivity in Scientific Inquiry. Princeton, N.J.: Princeton University Press, 1990 (excerpts)
- Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press, 2018. (excerpts)
- D'Ignazio, Catherine and Lauren F. Klein. Data Feminism. Cambridge, MA: MIT Press, 2020. (excerpts)
- Johnson, Gabrielle. “Are algorithms value-free? Feminist theoretical virtues in machine learning”, Journal of Moral Philosophy
- Pruss, Dasha. 2021. “Mechanical jurisprudence and domain distortions: How predictive algorithms warp the law”, Philosophy of Science
- Fazelpour, S, Lipton, Z, Danks, D. 2021. “Algorithmic fairness and the situated dynamics of justice”, The Canadian Journal of Philosophy
- Birhane, A., Kalluri, P., et al. “The values encoded in machine learning research”
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| Changing subject? |
No |
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