Businesses Struggle to Understand AI Answers

Artificial intelligence (AI) systems can provide clear answers, but often struggle to explain where those answers came from. This can be a problem when the results influence customer messages or management decisions. To address this issue, companies need to focus on data provenance, which is the record of how information originated and changed.

Data provenance can help explain which material was available to the AI system, how that material was processed, and which version supported a particular output. This is crucial for businesses to investigate mistakes, maintain useful information, and judge when an answer deserves human review.

The goal is proportionate traceability, which means connecting important outputs with identifiable inputs without reproducing every internal calculation. This requires a reliable way to connect outputs with identifiable inputs, while recognizing that a source record alone cannot establish whether the resulting answer is correct.

The W3C PROV overview provides a foundation for exchanging records about where data came from and how it was generated. In a business setting, the relevant history might include the original document, its owner, the date it became effective, and subsequent changes.

A company using AI to answer questions about customer service arrangements might need to keep a large archive of documents, but without source classification, the system may encounter conflicting material. Source classification involves approval status, intended audience, and effective dates, which can help the application select material and provide a basis for human review.