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New guidelines aim to make ISO data quality metrics executable for AI

By PulseAugur Editorial · [1 sources] ·

A new paper proposes implementation guidelines to make data quality (DQ) metrics from ISO/IEC 25024 and ISO/IEC 5259 executable. The authors address the gap between textual definitions of DQ dimensions and the low-level checks typically found in DQ tools. They classify metrics into generalizable, parameterized, and non-generalizable categories, providing guidelines for the automatable ones. The proposed implementation is realized in the open-source library `dqmeasure`, which learns parameters from reference data, demonstrating monotonic score decreases with injected errors and a correlation with downstream machine learning performance. AI

IMPACT Enables more reliable data quality assessment for AI/ML models by operationalizing ISO standards.

RANK_REASON The item is a research paper detailing new implementation guidelines for data quality metrics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New guidelines aim to make ISO data quality metrics executable for AI

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The item is a research paper detailing new implementation guidelines for data quality metrics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Philipp Jung, Katinka Becker, Felix Biessmann, Valerie Restat, Martin Seyferth, Daniel Schwabe, Lisa Ehrlinger ·

    Implementation Guidelines for Data Quality Metrics

    arXiv:2610.10919v1 Announce Type: cross Abstract: Despite decades of data quality (DQ) research, a gap remains between DQ dimensions, such as accuracy or completeness, which the literature defines in textual form, and DQ tools, which typically implement low-level checks that are …

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