When AI Helps Manage the Data, Who Checks Its Work?

Every organization has a file called “final,” another called “final revised,” and a third that someone swears is the one we actually use. Multiply that by years of shared drives, spreadsheets and databases, and it’s easy to see why artificial intelligence (AI) looks appealing. It can help sort through a mess that took humans years to make. This interesting topic came to us from UiPath in their article, “Software testers — your job isn’t disappearing. The grind is.“

AI can suggest tags for documents, flag likely duplicate records and spot unusual entries in large datasets. It can also help people find information by asking a question in plain language. For a team sorting through thousands of records, having AI suggest which ones need a closer look can save hours of manual review.

The catch is that a suggestion can look an awful lot like a decision once it shows up in a system. Two people with the same name might be flagged as duplicates. A draft policy might be labeled as current. And if an AI assistant can retrieve sensitive records that were shared too broadly, information that used to be buried in a folder suddenly becomes easy to find.

That’s where everyday data management earns its keep. Clear categories give AI a vocabulary for suggesting tags. Access controls determine what information it can retrieve for each person. An audit trail lets someone see why two records were flagged, and a designated reviewer can decide whether they should actually be merged.

The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

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Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.