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| Detailed information |
| Original study plan |
Bachelor's programme Artificial Intelligence 2025W |
| Learning Outcomes |
Competences |
| Students have the ability to critically analyze how gender and racial biases are embedded in algorithmic systems, applying theories from feminist philosophy of science and feminist data studies to identify ethical issues in AI design. They are able to evaluate and compare ethical perspectives and propose inclusive design modifications to address biases and promote fairness in AI tools.
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| Skills |
Knowledge |
- Defining Key Theories in Feminist Philosophy of Science and Data Studies (k2)
Students are able to define and outline key concepts from feminist philosophy of science and feminist data studies, understanding how these perspectives relate to computer science and engineering.
- Identifying Biases and Ethical Issues in Algorithmic Design (k4)
Students can identify gender, racial, and other biases in the design of algorithmic systems, recognizing the ethical implications these biases hold for marginalized groups.
- Comparing Ethical Perspectives on AI Design (k5)
Students are able to compare different ethical perspectives on algorithmic design, evaluating how these approaches influence the impact of AI systems on diverse populations.
- Proposing Inclusive and Ethical Design Approaches (k6)
Students can propose changes to AI system design that incorporate ethical considerations, aiming to create more fair, just, and inclusive algorithmic tools.
- Analyzing the Influence of Sociocultural Values on AI (k5)
Students are capable of analyzing how sociocultural values, often unintentionally, shape AI development and design, understanding the broader societal impact of these influences.
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Students know of feminist philosophy of science, feminist data studies, and ethical theories relevant to AI and algorithmic design, particularly in relation to gender and racial biases. They understand how biases enter AI systems and gain insights into strategies for ethical, inclusive, and fair design practices in computer science and engineering.
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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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