Three things to improve acceptance of AI

August 20, 2018

The bold future of UX: How new tech will shape the industry

Part 2  Three things to improve acceptance of AI

Artificial Intelligence (AI) is one of the hottest topics in tech right now. Conversations around AI inevitably lead to dreams of a world where a computer is predicting every need one might have and/or the impending doom of humanity through a SkyNet / Ultron / War Games-type scenario.

As entertaining as that discussion might be, instead I’m going to focus on what AI needs to do to become more functional and more accepted by society (that is, users). As it stands now, technology (including some of the advances in AI) seems to be advancing simply because developers want to see if they could build it. What my colleagues and I want to see, as user experience (UX) professionals, is meaningful advancements in AI that deliver functionality that is useful for users.

To meet this goal, I sat down with my colleague Gavin Lew, who has recently been talking a lot about AI, to identify three things that AI needs to be successful:

Context – At its core, AI is based on pattern-recognition. Once AI learns a pattern, it can make predictions about outcomes of similar patterns. However, while we’re giving AI the raw data it needs to recognize patterns, we’re not giving it the context in which to make good decisions. Our take on this is that we are doing a disservice to AI by not giving it the proper context.

  • An example of this is IBM Watson Health. IBM Watson for Oncology was fed data from the Sloan Kettering Cancer Center and then suggested treatments for various cancer types all over the world. It was able to suggest the correct treatment for lung cancer over 96% of the time in India. However, in South Korea, it was only correct 49% when suggesting treatments for gastric cancer. Why? Because South Korea’s treatments for gastric cancer aren’t in line with Sloan Kettering’s recommended treatments. In other words, Watson was lacking the context needed to suggest the right treatment approach.

Interaction – Our understanding of user interactions with AI is still developing. The user interactions of AI are largely still unknown to most. How is someone supposed to use AI? Is “use” even the right term when it comes to AI? Once it is fully realized, a complex AI system will entail the systems of a home, car, office, appliances, and personal tech gadgets, all talking to each other and exchanging information without the user having to actively do anything. Thus, the user is seemingly not doing anything to use AI, while the system itself is passing and parsing data behind the scenes.

  • Think ahead to the future where you have your own personal AI. Our interactions with AI may consist of nothing more than an offhand comment, essentially interacting with the AI without knowing that we’re doing so. For example, when I’m making breakfast and mutter to myself, “Almost out of milk,” a strong AI will know to remind me at an appropriate time to buy milk. Or maybe it will just take the initiative and order me a gallon of milk from the automated grocery service in my area and there will be a milk delivery timed for when I get home from work. Or maybe I don’t need to state that I’m out of milk for the AI to act…. perhaps finishing the gallon of milk is my passive interaction and the AI figures out what the next logical step is by ordering automatically.

Trust – Trust in AI has been a recent topic of discussion in the tech sphere. For people to want to use AI on a regular basis, they need to trust it. The early buggy interactions people had with Siri scared them away from voice assistants to the point that most have not attempted to try Microsoft’s Cortana. A new form factor (i.e., Alexa) finally encouraged people to give voice assistants (read: AI) a second chance, and it was more widely accepted and used.

  • But why? Because of trust. Trust is created when a question is asked, and the right answer is given, when a task is given and correctly performed, when a purchase is made and the correct product was bought, and, possibly most importantly, when personal info is kept safe.

Once AI has the three components of context, interaction, and trust, it will be much easier for it to hit the mainstream and be the runaway success that futurists predict it will be. Even if the above three pillars are never fully recognized, to truly deliver on the promise of AI to the end users, the developers of AI systems need to keep the end users in mind since the AI is ultimately being created to benefit them.

What are your thoughts on all of this? Comment below and let’s get a dialogue started!

This blog post part two of a series, The bold future of UX: How new tech will shape the industry, that will discuss future technologies and some of the issues and challenges that will face the user and the UX community. Read Part 1 that discussed Singularity and the associated challenges with UX design.