Situating methods in the magic of Big Data and AI

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Between Subjectivity and Imposition (2020, background): “This perspective sheds light on the situated [33, 66] and collaborative [35, 67] nature of data work, as clients and annotation teams both participate in the creation of datasets.”
Talking AI into Being: The Narratives and Imaginaries of National AI Strategies and Their Performative Politics (2021, background): “This industry agenda-setting favors an overhyped vision of AI, resulting in a public focus on potentials of AI and neglecting its actual methodological limitations (Elish and boyd 2018).”
Truth from the machine: artificial intelligence and the materialization of identity (2021, background): “This constitutes an ‘ epistemological hazard ’ (Elish and Boyd 2018, 58), since the potential objectivity of AI often amounts to nothing more than a veneer of certainty, and moreover, creates alarming interpretive dynamics.”
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, background): “However, biased models may perpetuate disparities by favoring certain learning styles or cultural backgrounds, disadvantaging students from un-derrepresented or marginalized communities [124, 125].”
Epistemic values in feature importance methods: Lessons from feminist epistemology (2021, background): “Elish and boyd [25] argue that the act of developing machine learning models itself should be framed as a situated practice, since humans are engaged in personally interpreting data”