An Ethnography of Datasets: Difference between revisions
From Algolit
| Line 1: | Line 1: | ||
by Algolit | 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 | + | 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 | + | 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 visualized. 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 | + | With this work, we look at the datasets most commonly used by data scientists to train machine algorithms. What material do they consist of? Who collected them? When? For what reason? |
------------------------------------ | ------------------------------------ | ||
Revision as of 10:17, 14 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 visualized. 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 at the datasets most commonly used by data scientists to train machine algorithms. What material do they consist of? Who collected them? When? For what reason?
Concept & interface: Cristina Cochior