Data & Trust Alliance Introduces Data Provenance Standards

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Data & Trust Alliance Introduces Data Provenance Standards


The Gist

  • Cross-industry collaboration. Developed by experts from top organizations, new data provenance standards aim to ensure transparent and ethical AI data use.
  • Comprehensive metadata coverage. The standards encompass key aspects like source, legal rights, and data lineage, promoting operational efficiency and regulatory compliance.
  • Future impact on business and ethics. Adoption of these standards could lead to enhanced customer trust, innovation in marketing and responsible AI development.

What to do with all this artificial intelligence and data. One industry group thinks it has a good solution. A Data Provenance Standards initiative by the Data & Trust Alliance announced Nov. 30 introduces eight standards to bring transparency to dataset origins for data and AI applications. These proposed standards, developed by experts from 19 organizations, aim to help companies verify data trustworthiness and suitability for use.

A transparent robot head in a piece about AI transparency and data provenance.
A Data Provenance Standards initiative by the Data & Trust Alliance announced Nov. 30 introduces eight standards to bring transparency to dataset origins for data and AI applications.Balerina Stock on Adobe Stock Photos

And it just so happened to debut one year from the public release of ChatGPT, OpenAI’s generative AI chatbot that took AI mainstream — and also woke the world up to the ethical use of such technologies.

Who Created These Data Provenance Standards?

The proposed standards were developed by data, AI, ethics, compliance and legal experts from Data & Trust Alliance companies including:

  • AARP
  • American Express
  • Deloitte
  • Howso
  • Humana
  • IBM
  • Kenvue
  • Mastercard
  • Nielsen
  • Nike
  • Pfizer
  • Regions Bank
  • Transcarent
  • UPS
  • Walmart
  • Warby Parker.

All are members of the Data & Trust Alliance, a not-for-profit, cross-industry consortium that develops practices for the responsible use of data and AI.

“As businesses scale and accelerate the impact of AI with trusted data, it is necessary to ensure the technology is developed and deployed responsibly,” Rob Thomas, senior vice president, software and chief commercial officer, IBM and chair of the D&TA Data Provenance initiative, said in a statement. “These practical data provenance standards, co-created by senior practitioners across industry, are designed to help ensure AI workflows are not only compliant with ever-changing government regulations and free of bias, but also developed to generate increased business value. While the standards may not address every application of AI, we believe they fill an important, longstanding need.”

Related Article: Ethical AI in Practice: Shaping a Better Future



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