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How the Elements of Transparency, Trust and Personalization Can Generate Business Value


The digital world is fragile at times. We’re never quite sure why the reception bars on our smartphones sometimes vary wildly during a single, stationary call. Apps we rely on regularly crash for no apparent reason — and then boot up again.

It’s into this already delicate balance that artificial intelligence (AI) enters. Yet we understand even less about how AI applications and systems do their jobs than we do about the workings of our digital lives. 

No wonder trust in AI systems is such a hot topic: Machines that make decisions beyond our control can — and do — unnerve us far more than digital’s everyday mysteries. 

The disconnect throws our understanding of machines off balance. More important, it risks moving AI toward a structural failure: If we don’t trust AI systems to do their jobs, they will fall far short of their promise — and the benefits companies are looking to achieve.  

Transparency, trust, and personalization (TTP) provide the counterbalance AI needs.

  • Transparency. The whys behind decisions are often more important to us than the decisions themselves. For many of us, knowing the backstory provides the emotional and intellectual foundation we need to make sense of decisions.

    AI systems, however, are black boxes. They’re sometimes composed of deep neural networks that obscure the whys behind what they do. Most times, we don’t learn the backstory behind AI’s decisions, and that opaqueness is a looming problem that threatens to undercut AI’s value. 

    This need for transparency runs through all of our encounters with AI, whether as consumers, employees or Internet users. Wouldn’t it be cool to ask Amazon why it’s suggesting, say, the pair of running shoes that its recommendation engine just offered? What if we could quiz Siri about our Wi-Fi lags?

    That kind of bidirectional communication will make AI systems hum. It’s more than auditability. It lets us probe AI systems to find out who they are and what they know about us. It answers the whys.

  • Trust. While transparency helps us process the decisions we encounter, trusted relationships become the cornerstone for our lives. We carefully build our lives around trusted resources that include our family, friends, and advisors, and the companies we pay to meet our needs in food, insurance, banking, and leisure. We rely on them.

    As with any entity, AI needs to earn our trust.

    As businesses think through how to integrate AI into their organizations, the question isn’t whether machines can deliver next-best offers but whether they can fit into the complexity of the moment. Trust often derives from context and connection, not confidence in the suggestion itself. Can we trust the algorithm to be accurate and to fit the nuances of our lives and decisions?

  • Personalization. When “artificial” is added to human intelligence, AI, too, must lead to decisions that meet our needs in meaningful and contextual ways.

Figure 1

Treating AI as a point solution misses the flow of our natural personalization and decision making. The point solution may include a sensible recommendation but knowing when to deliver suggestions is as important as identifying the suggestions themselves.

If AI says the right thing but at the wrong time and in the wrong way, it’s a structural failure. AI needs contextual awareness. If we tell a bank’s chatbot that an elderly parent has passed away and we want to close the account, and the bot cheerfully responds with an offer of six free months, that’s an AI failure, a reflection of a system unsuccessfully trained to understand context. It lacks emotional intelligence, which is still an innate human quality. Without emotional intelligence, AI systems are unable to personalize. 

Looking Ahead: An AI Action Plan

AI is indeed everywhere and nowhere at the same time — and that’s its strength. By developing AI systems built on transparency, trust and personalization, businesses can create a path forward and make AI an integral part of their digital transformation. 

Meaningful action requires coordinating meaningful steps. A successful AI approach requires moving quickly and in the right direction. 

To learn how organizations can use TTP to create AI systems that anticipate their fit and acceptance by other systems as well as by the people who use them, read our white paper, “AI: Ready for Business,” visit the AI & Analytics section of our website or contact us.”

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How the Elements of Transparency, Trust and Personalization Can Generate Business Value