Towards fairer datasets

Kaiyu Yang · Klint Qinami · Li Fei-Fei · Jia Deng · Olga Russakovsky

254 citations0/0 files storedACM Conference on Fairness, Accountability, and Transparency (FAccT)

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Ethics and Social Impacts of AI Advanced Neural Network Applications Domain Adaptation and Few-Shot Learning

Abstract

Computer vision technology is being used by many but remains representative of only a few. People have reported misbehavior of computer vision models, including offensive prediction results and lower performance for underrepresented groups. Current computer vision models are typically developed using datasets consisting of manually annotated images or videos; the data and label distributions in these datasets are critical to the models' behavior. In this paper, we examine ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods. We consider three key factors within the person subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology: (1) the stagnant concept vocabulary of WordNet, (2) the attempt at exhaustive illustration of all categories with images, and (3) the inequality of representation in the images within concepts. We seek to illuminate the root causes of these concerns and take the first steps to mitigate them constructively.

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References

2012Fairness through awareness 2014Big Data's Disparate Impact 1968Concreteness, imagery, and meaningfulness values for 925 nouns.2015Certifying and Removing Disparate Impact 2018Mitigating Unwanted Biases with Adversarial Learning 1980Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints 2010Three naive Bayes approaches for discrimination-free classification

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2021Mapping value sensitive design onto AI for social good principles 2021On the genealogy of machine learning datasets: A critical history of ImageNet 2021A sociotechnical view of algorithmic fairness 2021Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases 2021Fair Attribute Classification through Latent Space De-biasing 2022Algorithmic fairness datasets: the story so far

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