c.f. Selbst et al. 2019. drawing on the last 40yrs of science & technology studies to address the same damn problems* in machine learning Fairness and Abstraction in Sociotechnical Systems https://dl.acm.org/doi/pdf/10.1145/3287560.3287598

To truly understand whether introduction of the technology improves fairness outcomes. it is not only necessary to understand the localized fairness concerns, as discussed above, but also how the technology interacts with a pre-existing social system.

* framing, portability, formalism, ripple effect, solutionism

good manifesto for slow tech https://arxiv.org/pdf/2507.19960 What Does ‘Human-Centred AI’ Mean? (15p.) https://bsky.app/profile/olivia.science/post/3lv5xzlbyzc2y > Something I hinged on to get to this what I describe: the Marxian fetishisation of artefacts is so complete in the case of AI that not only do we somehow conclude machines think, but we accept for them to think, speak, draw instead of us, while also thinking these are (expressions of) our thoughts.