Word-of-machine effect with recommendation engines

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.

Amazon rec engine

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.

Augmented intelligence

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.

Human recommendations

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.

Key takeaways

  • 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.

Using machine learning to develop your hypothesis

There are many applications for machine learning, but one of the most exciting is using it to create hypothesis to test. An article in the Economist, “Computer says ‘try this’,” discusses many of the ways computers are now creating hypothesis to move medicine, farming and even cooking forward.

There are already many projects using machine learning to generate valuable and novel hypotheses. One project, BrainSCANr, suggests research topics for neuro-scientists by looking at millions of peer-reviewed papers. Research published by Baylor College of Medicine researchers used machine-learning software to review over 150,000 papers on a technique to curb the growth of cancer (proteins called kinases). The algorithm led to seven new kinases that researchers had missed. The technique is even being used to analyze search terms in Bing and Internet Explorer to determine potentially dangerous pairings of medicine.

Machine learning generates strong hypotheses

What it means for you

While machine learning is already benefitting many tech and game companies, using it to help develop hypotheses for your business is invaluable. If you are a mobile game company, think of the value of having a machine-learning system suggest that rather than focusing on your premium exchange rate, you should test changing the frequency of your free-coin bonus. Continue reading “Using machine learning to develop your hypothesis”

Using machine learning to improve customer interaction

I recently read a very interesting post, How Machine Learning can Improve Customer Interaction, that does a great job of listing different ways you can leverage machine learning to communicate better with your customers. The ideas include:

Machine learning

  • A personalized approach when you visit a website. When you are on an e-commerce site or using a search engine, the host collects rich information on your behavior. Machine learning analyzes the data and transforms the website into something geared to the individual customer. Machine learning then will control what you see, what appears in a search bar, how the site communicates with you, to best meet your individual needs.
  • Making recommendations. Making recommendations relevant for the user was one of the first major consumer applications of machine learning. Virtually everyone has experienced Amazon’s recommendations, when you make a purchase it recommends products likely to resonate (and almost everyone has taken advantage of these recommendations). Automated personalization with machine learning takes information about the shopper, refines those recommendations and tailors them specifically to the individual shopper. As the article points out, “it is like having a salesperson with the customer the whole time, pointing out what products he or she thinks are right up the customer’s alley.”

Continue reading “Using machine learning to improve customer interaction”

Pushing the edge of machine learning

As part of a MOOC I just finished (Northwestern’s Content Strategy course, in which there was an interview with IBM’s SVP of the Watson Group, Mike Rhodin) that did a great job of showcasing the potential and future of machine learning. The Watson Group is probably most famous for developing the computer that won on Jeopardy! and it is now deploying that technology to push the boundaries of machine learning.

During the interview, Rhodin explained how Watson can read and understand text. As it can understand text, Watson can learn by reading or obtaining additional information that either will confirm or question a hypothesis. In the latter case, it can then seek out additional information to reach the most likely answer, including looking at historical research and results. It will then give a recommendation and confidence level based on all available information, with the supporting evidence and why the evidence is important. Watson will then analyze whether its recommendations were correct, learn from its mistakes, and effectively get smarter (e.g., why when it played Jeopardy! it got stronger as it completed a column).

Watson wins Jeopardy

Rhodin discussed how Watson is currently helping doctors make more accurate diagnoses. The doctor will tell Watson a patient’s symptoms (Watson can understand spoken English), then Watson will compare these symptoms with what it has read and what is in its knowledge base. It will then narrow down the possible causes and present that information to the doctor. Keep in mind that there is so much research being published daily (and even more historical research) that no doctor can stay on top of all of it. Once Watson has narrowed it down to a few possible causes, it will present these to the doctor with the evidence it generated. The doctor can either do their own research or it may trigger a memory of an article they read in the past. Watson has thus helped the doctor reach a diagnose faster, which often helps recovery rates and reduces treatment costs.

Continue reading “Pushing the edge of machine learning”