Building Robust AI Systems with Verified Data Inputs

Artificial intelligence is inherently dependent on the quality of data that powers it for it to function properly. However, this reliance presents a major challenge to the development of artificial intelligence. There is a recent report that indicates that approximately half of executives do not believe their data infrastructure is adequately prepared to handle the evolving demands of artificial intelligence technologies.

As part of the study, conducted by Dun & Bradstreet, executives of companies actively integrating artificial intelligence into their business were surveyed.

As a result of the survey, 54% of these executives expressed concern over the reliability and quality of their data, which was conducted on-site during the AI Summit New York, which occurred in December of 2017. Upon a broader analysis of AI-related concerns, it is evident that data governance and integrity are recurring themes.

Several key issues have been identified, including data security (46%), risks associated with data privacy breaches (43%), the possibility of exposing confidential or proprietary data (42%), as well as the role data plays in reinforcing bias in artificial intelligence models (26%) As organizations continue to integrate AI-driven solutions, the importance of ensuring that data is accurate, secure, and ethically used continues to grow.

AI applications must be addressed as soon as possible to foster trust and maximize their effectiveness across industri

[…]
Content was cut in order to protect the source.Please visit the source for the rest of the article.