How Behavioural Economics Can Improve AI Decision-Making
Having developed products that require users to change behaviours, I've learned the immense value of applying principles from behavioural science. When designing payment journeys for managing debt or applying for credit, simplifying and clarifying the steps for users helped tremendously in achieving their financial goals. Now, I believe a similar approach can enhance artificial intelligence.
The primary aim of AI is to enable machines to make decisions based on data. However, traditional AI models lack understanding of the irrationality and cognitive biases prevalent in human behaviour. This is where behavioural economics can make a difference.
Behavioural economics combines principles from psychology and economics to better understand how people make decisions. It takes into account our mental shortcuts and the social, emotional, and cognitive factors that subconsciously influence us. When applied to AI, behavioural economics can lead to more empathetic and human-centric decision-making.
Current AI models largely rely on algorithms and data, overlooking how “noisy” human behaviour can be. As Daniel Kahneman highlights in Thinking, Fast and Slow, we are prone to taking mental shortcuts and making instinctive gut reactions rather than purely logical choices. AI systems built without accounting for this can make recommendations that would seem bullish.
Some real-world examples demonstrate this limitation. AI scheduling tools may overload employees with back-to-back meetings without considering human constraints. Recommendation engines may make suggestions based on past purchases without realizing when users' needs have changed. Lacking an understanding of behavioural context can clearly impact user experience.
So how can we bridge this gap? Behavioural economics provides frameworks to identify relevant psychological, social, and emotional factors that influence decision-making. Integrating this can enable AI to mirror more intuitive human planning.
In practice, behavioural AI could have wide-ranging applications. Tailoring recommendations by predicting when users will be most receptive; nudging healthcare patients towards beneficial behaviours without seeming paternalistic; or building financial models that account for irrational biases. User testing will be key to validating these concepts with qualitative feedback.
Of course, the ethical application of behavioural science principles will be critical. The goal should be enhancing user experience - not manipulation. With thoughtful implementation, behavioural AI can factor in the context missing from data and traditional algorithms. It's an idea I encourage businesses to explore further. The human-centric insight has so much potential to unlock.