Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics

By Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj, David Krueger, Sara Hooker. 2022

36 citations recorded by the source corpus.

Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or raw data, practitioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with these challenges tend to make strong assumptions about the particular issues at play, and often require a priori knowledge or metadata such as domain labels. Our work is orthogonal to these methods: we instead focus on providing a unified and efficient framework for Metadata Archaeology -- uncovering and inferring metadata of examples in a dataset. We curate different subsets of data that might exist in a dataset (e.g. mislabeled, atypical, or out-of-distribution examples) using simple transformations, and leverage differences in learning dynamics between these probe suites to infer metadata of interest. Our method is on par with far more sophisticated mitigation methods across different tasks: identifying and correcting mislabeled examples, classifying minority-group samples, prioritizing points relevant for training and enabling scalable human auditing of relevant examples.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Optimization & Training, Evaluation & Benchmarks

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Citations in the corpus

Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face (2023, background): “Being unable to easily audit large datasets incentivizes researchers to release models trained on data they do not truly understand [21] leading to model behaviors that are hard to study, predict or trace [3, 21, 29].”
On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research (2023, unknown): “Moreover, with the ever-expanding size of datasets, auditing for toxicity has become infeasible for human annotators (Veale and Binns, 2017; Jhaver et al., 2019; Siddiqui et al., 2022).”
Stable Bias: Evaluating Societal Representations in Diffusion Models (2023, unknown): “…are many sources from which biases can originate in TTI systems; for instance, the training data used for training diffusion such as Stable Diffusion is scraped from the Web and has been shown to contain harmful and pornographic content [11, 37] as well as mislabeled and corrupted examples [59].”