Classification, Slippage, Failure and Discovery
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 53 references that are in the map. First posted on arXiv 8 Apr 2021.
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9 of 53 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
- 2015SSD: Single Shot MultiBox Detectorcited by 3,125
- 2020Artificial Intelligence for Social Good: A Surveycited by 29
- 2019Can You Trust This Prediction? Auditing Pointwise Reliability After Learningcited by 28
- 2019Testing DNN Image Classifiers for Confusion & Bias Errorscited by 17
- 2019Artificial Intelligence for Social Goodcited by 8
- 2019Neural Networks Trained on Natural Scenes Exhibit Gestalt Closurecited by 6
- 2020Machine Learning in Ethnobotany -- a first experimentcited by 2
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