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Advances, challenges and opportunities in creating data for trustworthy AI

Nature Machine Intelligence volume 4, pages 669–677 (2022)Cite this article

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An Author Correction to this article was published on 21 September 2022

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Abstract

As artificial intelligence (AI) transitions from research to deployment, creating the appropriate datasets and data pipelines to develop and evaluate AI models is increasingly the biggest challenge. Automated AI model builders that are publicly available can now achieve top performance in many applications. In contrast, the design and sculpting of the data used to develop AI often rely on bespoke manual work, and they critically affect the trustworthiness of the model. This Perspective discusses key considerations for each stage of the data-for-AI pipeline—starting from data design to data sculpting (for example, cleaning, valuation and annotation) and data evaluation—to make AI more reliable. We highlight technical advances that help to make the data-for-AI pipeline more scalable and rigorous. Furthermore, we discuss how recent data regulations and policies can impact AI.

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Fig. 1: Comparison of model-centric versus data-centric approaches in AI.
Fig. 2: Roadmap for data-centric method development from data design to evaluation.
Fig. 3: Illustrations of methods for data valuation, data programming, data augmentation and data ablation.

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Acknowledgements

We thank T. Hastie, R. Daneshjou, K. Vodrahalli and A. Ghorbani for discussions. J.Z. is supported by a NSF CAREER grant.

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Authors and Affiliations

  1. Department of Computer Science, Stanford University, Stanford, CA, USA

    Weixin Liang, L. Fei-Fei, Matei Zaharia & James Zou

  2. IBM Research – Africa, Nairobi, Kenya

    Girmaw Abebe Tadesse

  3. Stanford Law School, Stanford University, Stanford, CA, USA

    Daniel Ho

  4. Department of Computer Science, ETH Zurich, Zurich, Switzerland

    Ce Zhang

  5. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    James Zou

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Liang, W., Tadesse, G.A., Ho, D. et al. Advances, challenges and opportunities in creating data for trustworthy AI. Nat Mach Intell 4, 669–677 (2022). https://doi.org/10.1038/s42256-022-00516-1

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