Security News > 2024 > July > Cross-industry standards for data provenance in AI
In this Help Net Security interview, Saira Jesani, Executive Director of the Data & Trust Alliance, discusses the role of data provenance in AI trustworthiness and its impact on AI models' performance and reliability.
Can you explain why data provenance is critical for AI trustworthiness and how it impacts AI models' overall performance and reliability?
Data provenance provides transparency into the origin, lineage, and rights associated with datasets-used in both AI and traditional data applications.
Clear data provenance can also reduce data scientists' time on data preparation and cleansing tasks.
This diverse group of contributors with functions including chief technology officers, chief data officers, and leaders in data governance, data acquisition, data quality, privacy, legal, and compliance ensured that the standards address common challenges and needs across multiple industries.
As AI systems become more integrated into various sectors, the adoption of these standards can help ensure that data used in AI applications is reliable and legally compliant, thereby mitigating risks related to privacy, copyright, and brand protection.