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The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence

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arxiv 2201.03954 v2 pith:6JAKOKA7 submitted 2022-01-10 cs.LG cs.AI

Kasia S. Chmielinski , Sarah Newman , Matt Taylor , Josh Joseph , Kemi Thomas , Jessica Yurkofsky , Yue Chelsea Qiu This is my paper
classification cs.LGcs.AI
keywords labeldatanutritiondatasetdatasetsdesignlaunchingmitigate
verification ladder T0 review T1 audit T2 compute T3 formal
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As the production of and reliance on datasets to produce automated decision-making systems (ADS) increases, so does the need for processes for evaluating and interrogating the underlying data. After launching the Dataset Nutrition Label in 2018, the Data Nutrition Project has made significant updates to the design and purpose of the Label, and is launching an updated Label in late 2020, which is previewed in this paper. The new Label includes context-specific Use Cases &Alerts presented through an updated design and user interface targeted towards the data scientist profile. This paper discusses the harm and bias from underlying training data that the Label is intended to mitigate, the current state of the work including new datasets being labeled, new and existing challenges, and further directions of the work, as well as Figures previewing the new label.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 28 citations worldwide. Full citation record

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    In interviews with 11 Portuguese-language model developers, four AI ethics tools guided general ethical reflection but failed to surface Portuguese-specific harms like cultural misrepresentation and low language performance.

  3. A case for data valuation transparency via DValCards

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    Data valuation is unstable across imputation methods and can penalize minority groups; the paper proposes DValCards to document and constrain such valuation use.

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