An Ethnography of Datasets: Difference between revisions
From Algolit
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| + | In the transfer of bias from a societal level to the machine level the dataset seems to be overlooked as an intermediate stage in decision making: the parameters by which a social environment is boxed into are determined by various factors. In the | ||
creation of datasets that form the basis on which computer models function, conflict and ambiguity are neglected in favour of making reality computable. Data collection is political, but its politics are rendered invisible in the way it is presented and visualised. Datasets are not a distilled version of reality, nor simply a technology in itself. But as any technology, datasets encode their goal, their purpose and the world view of the makers. | creation of datasets that form the basis on which computer models function, conflict and ambiguity are neglected in favour of making reality computable. Data collection is political, but its politics are rendered invisible in the way it is presented and visualised. Datasets are not a distilled version of reality, nor simply a technology in itself. But as any technology, datasets encode their goal, their purpose and the world view of the makers. | ||
With this work, we look into the most commonly used datasets for training machine learning and data scientists. What material do they consist of? Who collected them? When? For what reason? | With this work, we look into the most commonly used datasets for training machine learning and data scientists. What material do they consist of? Who collected them? When? For what reason? | ||
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| + | Concept & interface: Cristina Cochior | ||
[[Category:Data_Workers]][[Category:Data_Workers_EN]] | [[Category:Data_Workers]][[Category:Data_Workers_EN]] | ||
Revision as of 16:05, 1 March 2019
by Algolit
In the transfer of bias from a societal level to the machine level the dataset seems to be overlooked as an intermediate stage in decision making: the parameters by which a social environment is boxed into are determined by various factors. In the creation of datasets that form the basis on which computer models function, conflict and ambiguity are neglected in favour of making reality computable. Data collection is political, but its politics are rendered invisible in the way it is presented and visualised. Datasets are not a distilled version of reality, nor simply a technology in itself. But as any technology, datasets encode their goal, their purpose and the world view of the makers.
With this work, we look into the most commonly used datasets for training machine learning and data scientists. What material do they consist of? Who collected them? When? For what reason?
Concept & interface: Cristina Cochior