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What AI should be doing with documents

February 2024 by Duff Johnson (PDF Association)

Although Artificial Intelligence (AI) is beginning to dramatically transform how users interact with and process documents, weaknesses remain. Even when the training data is tightly quality controlled, AI’s results are not necessarily trustworthy.

Although AI is already assisting in authoring, data extraction, and content management, AI tools can only be as good as their inputs and training. If input data is biased, AI models are helpless to resolve – or even detect – the problem, a major source of AI hallucinations. But, as Air Canada, Michael Cohen, and many others have already realized, relying on AI for critical tasks should prompt far more due diligence than their ease of use implies. AI is a near-miraculous assistant, but trust must be earned, not given.

Competent processing of inputs is a sine qua non for competent, trustworthy AI. So far, however, we see AI developers relying on data volume and (apparently) ignoring data quality.

This article highlights the potential for AI authoring assistants to establish rich and trustworthy semantics and the significance of quality ingestion tools to leverage those trusted semantics. AI is a near-miraculous assistant, but trust must still be earned, not given.

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