Introducción: por qué la ciencia de datos necesita feminismo
data-feminism.mitpress.mit.edu
Introducción: por qué la ciencia de datos necesita feminismo
Datasets aren’t simply raw materials to feed algorithms, but are political interventions. As such, much of the discussion around 'bias' in AI systems misses the mark: there is no 'neutral,' 'natural,' or 'apolitical' vantage point that training data can be built upon. There is no easy technical 'fix' by shifting demographics, deleting offensive ter... See more
L. M. Sacasas • The Uncanny Gaze of the Machine
Como decía Alvin Toffler, «Los analfabetos del siglo XXI no serán aquellos que no sepan leer y escribir, sino aquellos que no sepan aprender, desaprender y reaprender».
Jordi Fortuny • Recupera tu vida con una mente extendida: El método definitivo de efectividad personal (Spanish Edition)
This two-cultures thinking, moreover, distorts the empirical realities of data collection, the challenging work of forcing unruly phenomena to speak in clean, distinct, ideally quantitative phrases
Melanie Feinberg • The Myth of Objective Data
With examples from technology to natural disasters, this is an original and timely reminder of why we need women in the leadership of the…
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Caroline Criado Perez • Invisible Women: the Sunday Times number one bestseller exposing the gender bias women face every day
If we tend toward the world in a feminist way, if we repeat that tending, again and again, we acquire feminist tendencies.
Sara Ahmed • Living a Feminist Life
Patterns when telling stories with data: - What is the dataset? Who generated the dataset and why? - What is the process that underpins the dataset? Given that process, what is missing from the dataset or has been poorly measured? Could other datasets have been generated, and if so, how different could they have been to the one that we have? - What
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