Modern data governance means reducing the friction between data consumers and the ability to utilize the data. This contemporary approach to data access management requires a company to have accurate and up-to-date data protection policies (DPP) containing the entire set of business rules, required regulations, and best practices. AI methods are required to build, maintain, and enforce the right policy so that the data can be utilized properly. To ensure reliability, the AI must be able to explain its decision (XAI). Explainability is a set of methods that allows the human users to understand and therefore trust the results created by the AI algorithms. Allowing data managers to oversee the automated decision-making is a key tenant of explainability. In addition, adding user feedback to the process can further enhance future results, as their feedback allows the AI to know what was right or wrong, enabling it to adjust and perform more accurately with every iteration.
CEO, University of Haifa