All Outputs (3)

A world model: On the political logics of generative AI (2024)
Journal Article
Amoore, L., Campolo, A., Jacobsen, B., & Rella, L. (2024). A world model: On the political logics of generative AI. Political Geography, 113, Article 103134. https://doi.org/10.1016/j.polgeo.2024.103134

The computational logics of large language models (LLMs) or generative AI – from the early models of CLIP and BERT to the explosion of text and image generation via ChatGPT and DALL-E − are increasingly penetrating the social and political world. Not... Read More about A world model: On the political logics of generative AI.

From rules to examples: Machine learning's type of authority (2023)
Journal Article
Campolo, A., & Schwerzmann, K. (2023). From rules to examples: Machine learning's type of authority. Big Data and Society, 10(2), https://doi.org/10.1177/20539517231188725

This paper analyzes the effects of a perceived transition from a rule-based computer programming paradigm to an example-based paradigm associated with machine learning. While both paradigms coexist in practice, we critically discuss the distinctive e... Read More about From rules to examples: Machine learning's type of authority.

Machine learning, meaning making: On reading computer science texts (2023)
Journal Article
Amoore, L., Campolo, A., Jacobsen, B., & Rella, L. (2023). Machine learning, meaning making: On reading computer science texts. Big Data and Society, 10(1), https://doi.org/10.1177/20539517231166887

Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better... Read More about Machine learning, meaning making: On reading computer science texts.