Recommendation engines have been around for years, it is one of the early drivers of Amazon.com’s success (first written about in 2003), but recently they have evolved from a competitive advantage to a cost of doing business. A recent article in the Harvard Business Review, When Do We Trust AI’s Recommendations More Than People’s? by Chiara Longoni and Luca Cian, provides useful advice for leveraging further AI-driven recommendation engines.
The central finding by Longoni and Cian is that accuracy is not the only element important in recommendation engines, but also context. They write, “simply offering AI assistance won’t necessarily lead to more successful transactions. In fact, there are cases when AI’s suggestions and recommendations are helpful and cases when they might be detrimental. When do consumers trust the word of a machine, and when do they resist it?”
Word-of-machine effect drives acceptance of AI recommendations
For recommendations to impact positively the customer experience, a key factor is what is being recommended. Longoni and Cian found that when customers focus on the functional and practical aspects of a product, its utilitarian value, they trust a machine. If they focus on the experiential and sensory aspects of a product, its hedonic value, they prefer and trust a human recommendation. If a customer is focused on utilitarian and functional qualities, then, from a marketer’s perspective, the word of a machine is more effective than the word of human recommenders. For someone focused on experiential and sensory qualities, human recommenders are more influential.
This phenomenon is referred to as word-of-machine effect. The authors write, “the word-of-machine effect stems from a widespread belief that AI systems are more competent than humans in dispensing advice when utilitarian qualities are desired and are less competent when the hedonic qualities are desired. Importantly, the word-of-machine effect is based on a lay belief that does not necessarily correspond to the reality.… The fact of the matter is humans are not necessarily less competent than AI at assessing and evaluating utilitarian attributes. Vice versa, AI is not necessarily less competent than humans at assessing and evaluating hedonic attributes.”
The word-of-machine effect extends to how people evaluate a product. If a product, like food (or a slot machine), that is evaluated based on hedonistic traits, is recommended by AI rather than a person, the consumer is more likely to discount its quality after trying it. In a study cited by Longoni and Cian, “[they] recruited 144 participants from the University of Virginia campus and informed them that we were testing chocolate-cake recipes for a local bakery. The participants were given two options: one cake created with ingredients selected by an AI chocolatier and one created with ingredients selected by a human chocolatier. Participants were then asked to eat one of the two cakes, which were identical in appearance and ingredients, and rate the cake for two experiential/sensory features (indulgent taste and aromas, pleasantness to the senses) and two utilitarian/functional attributes (beneficial chemical properties and healthiness). Participants rated the AI-recommended cake as less tasty but healthier than the cake recommended by the human chocolatier.”
Customers, however, trust AI-generated recommendations more when utilitarian features are most important. In another study, when consumers who were looking to purchase a winter coat were given recommendations based on practical features (warmth, breathability, etc), they preferred an AI shopping assistant over a human, and the more they cared about hedonic features, the more they preferred a human shopping assistant over an AI.
Another interesting finding by Longoni and Cian is that consumers will embrace AI recommendations if they believe a human was part of the recommendation process. In one experiment, the authors framed AI as augmented intelligence that enhances and supports human recommenders rather than replacing them. The AI-human hybrid recommender fared as well as the human-only recommender even when experiential and sensory considerations were important. Going back to Amazon, they are quite adept at combining human and AI recommendations. As part of their recommendation process, they will show customers what other customers like them have bought, thus it is effectively the other customers, not AI, making the recommendation.
Balancing word-of-machine effect
One way to mitigate word-of-machine effect is telling customers about the phenomenon (though wording it simpler than an academic would). Longoni and Cian found that, by prompting people to consider a different viewpoint about the recommender’s ability, it reduced the word-of-machine impact.
Another option that the authors found useful is using a chat bot. In one study, “[they] invited 299 online respondents to read about an app called ‘Cucina’ that would rely on AI to give recipe recommendations. Within the app, the participants were able to interact with a chat bot – an AI chef – that was programmed to assist them. The chat bot greeted each participant and introduced itself (‘Hi, Mark! I am here to suggest a recipe for you to try!’). The AI chef then delivered the consider-the-opposite protocol using a fun, interactive nudge: ‘Some people might think that an artificial intelligence chef is not competent to give food suggestions, but this is a misjudgment. For a moment, set aside your expectations about me. When it comes to making food suggestions, could you consider the idea that I could be good at things you do not expect me to be good at?’ That resulted in more favorable perceptions of the AI recommendation even when people were considering the experiential and sensory qualities of a recipe like taste and aromas.”
How to benefit from word-of-machine effect
As I mentioned earlier, effective AI recommendation engines have gone from being a competitive advantage to a must have feature of any online product. To provide the best customer experience, you need to understand the word-of-machine effect and biases it creates. It is important to remember these are biases, the AI can often give a better recommendation even if customers believe otherwise. Thus, you need to find a way to lead your customer to their best possible experience, without misleading them or creating the perception that it is a sub-optimal experience. The key is framing the recommendation in a way the customer will trust it, given what is being recommended.
- AI driven recommendation engines, popularized on Amazon and Netflix, have gone from being a competitive advantage in online and mobile apps to part of the cost of doing business.
- While AI recommendations are often closer to what customers want, people trust human recommendations more when it is based on hedonic or experiential factors, and trust machines more when it is based on utilitarian factors; this is referred to as word-of-machine effect.
- When presenting recommendations, you must be cognizant of word-of-machine effect; for recommendations around hedonistic properties try to bring in a human element (augmented intelligence) or explain that these are better recommendations.
3 thoughts on “Word-of-machine effect with recommendation engines”
Thank you for this article. 🙂 We’re a small retailer and have been looking into getting an online recommendation engine after reading about it on Data Hunters but we weren’t sure whether to use a DaaS provider or build one of our own.
I’ll leave your question for an expert to respond to but in my experience it should not be that hard to write it internally.