18 ways to check whether data can be trusted for AI
ETSI published TR 104 180 defining 18 data quality metrics, including fairness and privacy, to assess whether datasets are fit for AI.
ETSI's technical report TR 104 180 defines 18 metrics across four groups - intrinsic quality, usability and lineage, fairness, and privacy - each with calculation formulas, plus an open-source tool that scores datasets. Testing on an aircraft engine sensor dataset and a US census dataset revealed a roughly threefold gender gap in high earners (about 31% of men versus 11% of women) and two privacy failures: re-identification via age, race, sex, and country, and sensitive fields stored in plaintext. The working group included Sejong University, EGM, TTA, Daejeon University, and CNIT.
- 18 metrics grouped into completeness/accuracy, usability, fairness, and privacy
- Census data shows ~31% of men versus ~11% of women marked high earners
- Age, race, sex, and country combined were enough to re-identify individuals; sensitive data unencrypted
- Open-source tool scores any dataset against the 18 metrics
- Vendors
- ETSI
- Organizations
- Sejong UniversityCNITDaejeon UniversityTTAEGM
From the original article
2 min read at helpnetsecurity.com
ETSI has published TR 104 180, a technical report that defines 18 metrics for measuring data quality, giving companies a way to check whether their data is good enough for AI before they use it. The report defines each metric and includes the formulas needed to calculate it. The metrics fall into four groups.
By @helpnetsecurity · helpnetsecurity.com
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