Reading suggestions from:

NÄR SAMHÄLLET MATAS IN I EN AI (Flattening the world over a cup of coffee)

Bias from a biased world

Benjamin, R. 2019 Race After Technology Polity. https://www.ruhabenjamin.com/race-after-technology

Amaro, R. 2023 Black Technical Object. On Machine Learning and the Aspiration of Black Being. MIT Press. https://mitpress.mit.edu/9783956795633/the-black-technical-object/

Wachter-Boettcher, S. 2017. Technically wrong. Norton. https://wwnorton.com/books/Technically-Wrong/

Bowker, G. & Star, SL 1999. Sorting Things Out. MIT Press. https://mitpress.mit.edu/9780262522953/sorting-things-out/

Predictive policing, article by ProPublica https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

Bias from missing data

Buolamwini, J. & Gebru, T. 2018 Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf

(Explore the Gender Shades study here: http://gendershades.org )

Criado-Perez, C 2018. Invisible women. Vintage Books. https://carolinecriadoperez.com/book/invisible-women/

Legacy data and LLMs

Garg, L. Schiebinger, D. Jurafsky, J. Zou, Word embeddings quantify 100 years of gender and ethnic stereotypes, Proc. Natl. Acad. Sci. U.S.A. 115 (16) E3635-E3644, https://doi.org/10.1073/pnas.1720347115 (2018).

Gonen, H. & Golderg, Y 2019. Lipstick on a Pig. Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them https://aclanthology.org/N19-1061/

Hannah Devinney, Jenny Björklund, and Henrik Björklund. 2020. Semi-Supervised Topic Modeling for Gender Bias Discovery in English and Swedish. In Proceedings of the Second Workshop on Gender Bias in Natural Language Processing, pages 79–92, Barcelona, Spain (Online). Association for Computational Linguistics.

https://aclanthology.org/2020.gebnlp-1.8/

Data imbalances, Intersectionality, Valuation

Cockburn, C. 1983. Brothers. Male Dominance and Technological Change. Pluto Press

Crenshaw, K. (1989) “Demarginalizing the Intersection of Race and Sex” University of Chicago Legal Forum, 1989(1).http://chicagounbound.uchicago.edu/uclf/vol1989/iss1/8 

Cho, S., I. et al (2013) Toward a Field of Intersectionality Studies. Signs. 38(4):785-810

Valuation Studies (journal) https://valuationstudies.liu.se

Epstein, S. (2007). Inclusion: The politics of difference in medical research. University of Chicago Press

Mulinari, S 2023 Short-circuiting biology: Digital phenotypes, digital biomarkers, and shifting gazes in psychiatry. Big Data and Society

Synthetic data

Johnson, E. (2024) 'Intersectional hallucinations': why AI struggles to understand that a six-year-old can't be a doctor or claim a pension. The Conversation

Johnson, E. & S. Hajisharif (2024) The Intersectional Hallucinations of Synthetic Data. AI & Society

Creating data about our world in multiple ways

Costanza-Chock, S. (2020) Design Justice MIT Press

D’Ignazio & Klein (2019) Data Feminism. MIT Press.

AI for the state service sector

Falk, P. 2024. Assemble Care. Diss. https://kau.diva-portal.org/smash/record.jsf?pid=diva2%3A1892232&dswid=-9152

Irina Zakharova, Juliane Jarke & Anne Kaun (2024) Tensions in Digital Welfare States: Three Perspectives on Care and Control. In: Journal of Sociology 60(3), https://doi.org/10.1177/14407833241238312

Statskontoret 2023 Myndigheterna och AI https://www.statskontoret.se/publicerat/publikationer/publikationer-2024/myndigheterna-och-ai---en-studie-om-mojligheter-och-risker-med-att-anvanda-ai-i-statsforvaltningen/