Outputs (33)

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.

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.

Politics of data reuse in machine learning systems: Theorizing reuse entanglements (2022)
Journal Article
Thylstrup, N. B., Hansen, K. B., Flyverbom, M., & Amoore, L. (2022). Politics of data reuse in machine learning systems: Theorizing reuse entanglements. Big Data and Society, 9(2), https://doi.org/10.1177/20539517221139785

Policy discussions and corporate strategies on machine learning are increasingly championing data reuse as a key element in digital transformations. These aspirations are often coupled with a focus on responsibility, ethics and transparency, as well... Read More about Politics of data reuse in machine learning systems: Theorizing reuse entanglements.

Machine learning political orders (2022)
Journal Article
Amoore, L. (2023). Machine learning political orders. Review of International Studies, 49(1), 20-36. https://doi.org/10.1017/s0260210522000031

A significant set of epistemic and political transformations are taking place as states and societies begin to understand themselves and their problems through the paradigm of deep neural network algorithms. A machine learning political order does no... Read More about Machine learning political orders.

The deep border (2021)
Journal Article
Amoore, L. (2024). The deep border. Political Geography, 109, Article 102547. https://doi.org/10.1016/j.polgeo.2021.102547

Deep neural network algorithms are becoming intimately involved in the politics of the border, and are themselves bordering devices in that they classify, divide and demarcate boundaries in data. Deep learning involves much more than the deployment o... Read More about The deep border.

Datawars: reflections twenty years after 9/11 (2021)
Journal Article
Amoore, L., & de Goede, M. (2021). Datawars: reflections twenty years after 9/11. Critical Studies on Terrorism, 14(4), 425-429. https://doi.org/10.1080/17539153.2021.1982117

Cloud Ethics: Algorithms and the Attributes of Ourselves and Others (2021)
Journal Article
Dwyer, A. C., O'Grady, N., Thornton, P., Straube, T., Gilbert, E., & Amoore, L. (2021). Cloud Ethics: Algorithms and the Attributes of Ourselves and Others. The AAG Review of Books, 9(3), 36-49. https://doi.org/10.1080/2325548x.2021.1921458

Doubt and the Algorithm: On the Partial Accounts of Machine Learning (2019)
Journal Article
Amoore, L. (2019). Doubt and the Algorithm: On the Partial Accounts of Machine Learning. Theory, Culture and Society, 36(6), 147-169. https://doi.org/10.1177/0263276419851846

In a 1955 lecture the physicist Richard Feynman reflected on the place of doubt within scientific practice. ‘Permit us to question, to doubt, to not be sure’, proposed Feynman, ‘it is possible to live and not to know’. In our contemporary world, the... Read More about Doubt and the Algorithm: On the Partial Accounts of Machine Learning.

Introduction: Thinking with Algorithms: Cognition and Computation in the Work of N. Katherine Hayles (2019)
Journal Article
Amoore, L. (2019). Introduction: Thinking with Algorithms: Cognition and Computation in the Work of N. Katherine Hayles. Theory, Culture and Society, 36(2), 3-16. https://doi.org/10.1177/0263276418818884

In our contemporary moment, when machine learning algorithms are reshaping many aspects of society, the work of N. Katherine Hayles stands as a powerful corpus for understanding what is at stake in a new regime of computation. A renowned literary the... Read More about Introduction: Thinking with Algorithms: Cognition and Computation in the Work of N. Katherine Hayles.

Securing with Algorithms: Knowledge, Decision, Sovereignty (2016)
Journal Article
Amoore, L., & Raley, R. (2017). Securing with Algorithms: Knowledge, Decision, Sovereignty. Security Dialogue, 48(1), 3-10. https://doi.org/10.1177/0967010616680753

Amid the deployment of algorithmic techniques for security – from the gathering of intelligence data to the proliferation of smart borders and predictive policing – what are the political and ethical stakes involved in securing with algorithms? Takin... Read More about Securing with Algorithms: Knowledge, Decision, Sovereignty.