Classification, Slippage, Failure and Discovery
9th Conference on Computation, Communication, Aesthetics & X 2021 This text argues for the potential of machine learning infused classification systems as vectors for a technically-engaged and constructive technology critique. The text describes this potential with several experiments in image d...
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| creator | Böhlen, Marc |
| description | 9th Conference on Computation, Communication, Aesthetics & X 2021 This text argues for the potential of machine learning infused classification
systems as vectors for a technically-engaged and constructive technology
critique. The text describes this potential with several experiments in image
data creation and neural network based classification. The text considers
varying aspects of slippage in classification and considers the potential for
discovery - as opposed to disaster - stemming from machine learning systems
when they fail to perform as anticipated. |
| doi_str_mv | 10.48550/arxiv.2104.03886 |
| format | Article |
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systems as vectors for a technically-engaged and constructive technology
critique. The text describes this potential with several experiments in image
data creation and neural network based classification. The text considers
varying aspects of slippage in classification and considers the potential for
discovery - as opposed to disaster - stemming from machine learning systems
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systems as vectors for a technically-engaged and constructive technology
critique. The text describes this potential with several experiments in image
data creation and neural network based classification. The text considers
varying aspects of slippage in classification and considers the potential for
discovery - as opposed to disaster - stemming from machine learning systems
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systems as vectors for a technically-engaged and constructive technology
critique. The text describes this potential with several experiments in image
data creation and neural network based classification. The text considers
varying aspects of slippage in classification and considers the potential for
discovery - as opposed to disaster - stemming from machine learning systems
when they fail to perform as anticipated.</abstract><doi>10.48550/arxiv.2104.03886</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computers and Society Computer Science - Learning |
| title | Classification, Slippage, Failure and Discovery |
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