How to Appease Your Customers After Your Algorithm Rejects Them
From a customer perspective, the only thing more frustrating than being denied a product or service is when that denial comes without a satisfactory explanation. As humans, our ability to deal with disappointment depends upon understanding why it happened. Without an acceptable rationale, we are apt to assume the worst: deliberate disrespect, blind prejudice, etc.
This aspect of consumer psychology may create problems for companies relying on decision-making algorithms for vetting purposes, fraud prevention and general customer service. We’re seeing widening adoption of AI in fields such as marketing and financial services. On balance, this is great news, allowing companies to serve customers with unprecedented speed and predictive precision. However, while bots beat humans hands down at making accurate decisions at scale, their communication skills (so far, anyway) leave much to be desired. As algorithms assume a more prominent role as gatekeepers, where will rejected customers turn for an adequate explanation? And how can companies provide one without revealing too much about their proprietary algorithms – which are, very often, essential IP?
Too many firms have not yet thought seriously about these questions – but policymakers have. Articles 13 to 15 of the EU’s General Data Protection Regulation require that companies using automated decision making supply customers with “meaningful information about the logic involved”. Determining what qualifies as “meaningful information” is slippery enough for commonplace decision-tree algorithms. As more sophisticated tools such as “deep learning” neural networks gain wider business application, the byzantine processes of the algorithms themselves may defy explanation.
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