“Humans respond to incentives too, but they are also influenced by nudges.”Richard Thaler and Cass R. Sunstein, Nudge (2008)
Human beings are surrounded by influences that tend to alter their behaviours. This is what Richard Thaler and Cass Sunstein’s research on Nudge Theory is essentially about. The nudges here refer to how choices are presented to individuals by a ‘choice architect’—to influence their preferences—without actually forbidding them from making other choices. A pretty basic and prevalent example of a nudge is paying 999 rupees instead of 1000. The former figure makes it seem like we’ve saved a great deal when we really haven’t. Nudge Theory then works on the logic that humans are not always rational in making decisions.
Where else could we be potentially nudged? Imagine being silently pushed towards making healthier eating choices or following road rules diligently. Both of these—and more—are already taking place around us. Nudging is ubiquitous and now, AI has jumped the bandwagon of turning into a choice architect.
In fact, smart nudging could help you make choices that generate better outcomes for yourselves.. As per Bob Suh, founder and CEO of OnCorps—which provides AI-based guidance to the financial services industry—the key here is to have AI mimic how human beings behave in constructive relationships.
How digital nudges can be game changers
Digital Nudges would be defined as using user-interface design elements to guide behaviours in digital choice environments. In today’s well connected world, one could be nudged via text, email, and even push notifications, and AI algorithms sit at the core of these nudges. For instance, many users tend to absentmindedly open and use Instagram on their phones after receiving a push notification from the app. Other examples include Netflix’s algorithm playing the next episode immediately—it would not take too long to switch it off. Still, most people are likely to continue watching. Also, Google’s auto-suggest options may encourage someone to click on certain searches.
Today, many machine learning algorithms incorporate behavioural science studies to do just this. Research conducted in the Journal of Business Research used such AI tools to determine whether they helped their users.
Namu’s ALEX Posture tracker
The ALEX Posture tracker is a wearable device that encourages people to maintain a good posture by measuring their neck angles. If a user’s neck bends too far into a poor angle, ALEX gently vibrates to alert its user. Such tools eventually allow their users to subconsciously think about maintaining their posture, thereby indicating that the nudging worked.
HAPIfork by Hapi
Another tool, HAPIfork by Hapi, is an intelligent fork that helps people monitor their eating habits. The tool alerts people through lights and gentle vibrations when they are eating too fast. These tools use a technique that entails noting pattern breaks and nudging users to receive better behaviours from them. Another technique entails encouraging self-awareness within people through rewards. We can also see an example of this in the earlier mentioned study:
Jenoptik Speed Sensors
Jenoptik Speed Sensors— by the German optical products company Jenoptik—use a radar sensor to measure the speed of a vehicle accurately. Its AI-powered RADARLUX tool rewards drivers who follow the required speed limit by showing them a smiley face.
A third technique is by choosing what time a choice needs to be presented to users. For instance, decisions that require more thought could be shown when the user has more time—perhaps through scheduling. Doing this seems innocuous, but it is just AI-powered nudging. The AI does nothing but presents the choice at a different time, but doing so has a different effect on the user than another time would. For example, many running apps send push notifications to users around the time they usually work out. Past precedence tells the machine that the user is generally accessible at that particular time. This strategy also makes use of the ‘noting pattern breaks and nudging’ method.
The key to these tools is that they allow users to make their own decisions. CaféWell Concierge is a health and nutrition platform from Welltok (powered by IBM Watson) that enables consumers to visualise their data over time and make their own decisions—whilst providing prompt guidance at the time of making a choice. This includes autonomy while also letting the AI nudge users into making healthier decisions. Intelligent nudges work effectively by providing sensitivity to decision-making processes. Mabu is a healthcare robot from Catalia Health that helps patients deal with chronic illnesses. Its users reported feeling supported by the intelligent platform. It allowed them to self-check and monitor their data for doctors and engage in conversations tailored to the user’s personality—something that affected their emotions and uplifted their wellbeing.
Despite the beautiful ways AI nudging could benefit us, many people have privacy concerns and are scared AI nudging could be manipulative. Let’s talk about the former first. Most of the apps explained above could require user information—ranging from dietary intake to schedules to entire personalities (such as in Mabu’s case). It is undoubtedly scary to consider such sensitive data falling into the wrong places. Additionally, while these tools do allow the user to make their own decisions, nudging is psychologically manipulative to an extent. What makes it worse is that the user may not always be aware that machine algorithms are carefully nudging their choices.
Nonetheless, these AI tools have benefited a lot of people. Developers of these tools can perhaps work on making them more secure for users on the privacy stand. Other than that, considering that our decisions have most likely constantly been nudged by our environment, we might as well let AI use it to let us make better choices.
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I am an economics undergrad who loves drinking coffee and writing about technology and finance. I like to play the ukulele and watch old movies when I'm free.