Myth: AI understands me, but I can’t understand it
Everyone can and should understand how AI works, so that—rather than be intimidated or misled by algorithmic decision-making—we can contribute multiple perspectives to designing and implementing the systems that impact us all differently.
AI understands me, but I can’t understand it.
AI ist NOT smarter than us. AI should be understandable and accessible.
Watch the talk
|Folien der Präsentation|
Crawford, K. & Paglen, T. (2019, September 19). Excavating AI: The Politics of Images in Machine Learning Training Sets.
Timnit Gebru. (2021, April 14). The Hierarchy of Knowledge in Machine Learning & Related Fields and Its Consequences.
Zubarev, V. (2018, November 21). Machine Learning for Everyone.
Griffith, C. (2017). Visualizing Algorithms.
Kogan, G. (n.d.). Neural networks. Retrieved 18 May 2021.
McPherson, T., & Parham, M. (2019, October 24). ‘What is a Feminist Lab?’ Symposium.
|UNICORN IN THE FIELD|
Algorithmic Justice League
Color Coded LA
Data Nutrition Project
School of Machines, Making, & Make-Believe
About the author
Sarah Ciston (she/they) is a Virtual Fellow at the Humboldt Institute for Internet and Society, and a Mellon Fellow and PhD Candidate in Media Arts + Practice at University of Southern California. Their research investigates how to bring intersectionality to artificial intelligence by employing queer, feminist, and anti-racist ethics and tactics. They lead Creative Code Collective—a student community for co-learning programming using approachable, interdisciplinary strategies. Their projects include a machine-learning interface that ‘rewrites’ the inner critic and a chatbot that explains feminism to online misogynists. They are currently developing a library of digital-print zines on Intersectional AI.
This post is part of our project “Why, AI?”. It is a learning space which helps you to find out more about the myths and truths surrounding automation, algorithms, society and ourselves. It is continuously being filled with new contributions.
This post represents the view of the author and does not necessarily represent the view of the institute itself. For more information about the topics of these articles and associated research projects, please contact email@example.com.
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