# "Excavating AI" Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset

arXiv: https://arxiv.org/abs/2107.13998 · HTML: https://mlsanity.com/paper/2107.13998/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)](https://mlsanity.com/paper/2009.05938/) (2020)
- [Cross-Cultural and Cultural-Specific Production and Perception of Facial Expressions of Emotion in the Wild](https://mlsanity.com/paper/1808.04399/) (2018)
- [Excavating "Excavating AI": The Elephant in the Gallery](https://mlsanity.com/paper/2009.01215/) (2020)
- [New interfaces for musical expression](https://mlsanity.com/paper/2010.14228/) (2020)
- [Dimensional Affect and Expression in Natural and Mediated Interaction](https://mlsanity.com/paper/1707.09599/) (2017)
- [A Mimetic Strategy to Engage Voluntary Physical Activity In Interactive Entertainment](https://mlsanity.com/paper/1709.00904/) (2017)
- [Suspect AI: Vibraimage, Emotion Recognition Technology, and Algorithmic Opacity](https://mlsanity.com/paper/2009.00502/) (2020)
- [Facial gesture interfaces for expression and communication](https://mlsanity.com/paper/2010.01567/) (2020)

## Built on by (most cited first, 12 of 12)

- [Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition](https://mlsanity.com/paper/2303.15889/) (2023)
- [Fairness and Bias Mitigation in Computer Vision: A Survey](https://mlsanity.com/paper/2408.02464/) (2024)
- [ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning](https://mlsanity.com/paper/2305.01486/) (2023)
- [DSAP: Analyzing Bias Through Demographic Comparison of Datasets](https://mlsanity.com/paper/2312.14626/) (2023)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [GCF: Graph Convolutional Networks for Facial Expression Recognition](https://mlsanity.com/paper/2407.02361/) (2024)
- [Neural Architecture Search Using Genetic Algorithm for Facial Expression Recognition](https://mlsanity.com/paper/2304.12194/) (2023)
- [A Perception CNN for Facial Expression Recognition](https://mlsanity.com/paper/2512.06422/) (2025)
- [Ada-DF: An Adaptive Label Distribution Fusion Network For Facial Expression Recognition](https://mlsanity.com/paper/2404.15714/) (2024)
- [GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution](https://mlsanity.com/paper/2410.15927/) (2024)
- [Calibrating Practical Privacy Risks for Differentially Private Machine Learning](https://mlsanity.com/paper/2410.22673/) (2024)
- [HQFNN: A Compact Quantum-Fuzzy Neural Network for Accurate Image Classification](https://mlsanity.com/paper/2506.11146/) (2025)

## Abstract (arXiv)

Twenty-five years ago, my colleagues Miyuki Kamachi and Jiro Gyoba and I designed and photographed JAFFE, a set of facial expression images intended for use in a study of face perception. In 2019, without seeking permission or informing us, Kate Crawford and Trevor Paglen exhibited JAFFE in two widely publicized art shows. In addition, they published a nonfactual account of the images in the essay "Excavating AI: The Politics of Images in Machine Learning Training Sets." The present article recounts the creation of the JAFFE dataset and unravels each of Crawford and Paglen's fallacious statements. I also discuss JAFFE more broadly in connection with research on facial expression, affective computing, and human-computer interaction.