How Cognitive Bias In AI Impacts Business Outcomes - AI Summary


For instance, specific data that a neural network might not be able to process, such as the reasoning behind the results of an insurance claim -- might not have a straightforward representation in machine learning because of possible interpretations. This issue of overfitting is a typical problem of AI, and a variety of use cases, and data might bring up additional challenges that the human brain can handle and adapt to more easily and creatively. For example, if there are exceptions to the rules in issues of fraud detection in the financial industry, both experts and customers alike would want to know all of the elements that led to the AI's decision and require some transparency regarding the outcome. Few things are more frustrating for business owners than a missed target or a misplaced investment, but cognitive biases can hinder intelligent decisions and cost every year. But if your business faces a sudden uncertainty, a proclivity for deep thinking, over-analyzing, and compensating for lower performance through shortcuts doesn't help.

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