# Classification, Slippage, Failure and Discovery

arXiv: https://arxiv.org/abs/2104.03886 · HTML: https://mlsanity.com/paper/2104.03886/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Artificial Intelligence for Social Good: A Survey](https://mlsanity.com/paper/2001.01818/) (2020)
- [Can You Trust This Prediction? Auditing Pointwise Reliability After Learning](https://mlsanity.com/paper/1901.00403/) (2019)
- [Testing DNN Image Classifiers for Confusion & Bias Errors](https://mlsanity.com/paper/1905.07831/) (2019)
- [Artificial Intelligence for Social Good](https://mlsanity.com/paper/1901.05406/) (2019)
- [Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure](https://mlsanity.com/paper/1903.01069/) (2019)
- [Machine Learning in Ethnobotany -- a first experiment](https://mlsanity.com/paper/2008.01611/) (2020)

## 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.