Amazon recently introduced AI-generated customer review highlights, which present concise summaries of common themes and sentiments from written reviews, helping shoppers quickly gauge if a product suits their needs. The summarisation tool, currently undergoing testing since earlier this year, is available to select mobile users in the U.S. 

A new AI-generated feature enables product insights and allows easy access to reviews highlighting specific attributes of products like “ease of use” or “performance.” 

By leveraging AI to present review highlights and encouraging authentic feedback. Amazon strives to make the shopping journey clearer and more transparent for its customers. 

Essentially, the technology derives its functionality from Amazon’s Community Guidelines, which act as parameters for its machine learning models in analysing multiple data points to detect risks and expert investigators in fraud-detection techniques to prevent fake reviews. The analysis encompasses various data points, such as account relationships, sign-in activities, review histories, and indicators of unusual behaviour.

“Our goal is to ensure that every review in Amazon’s stores is trustworthy and reflects customers’ actual experiences. Amazon welcomes authentic reviews—whether positive or negative —but strictly prohibits fake reviews that intentionally mislead customers,” said David Montague, Vice President of Selling Partner Risk, at Amazon. 

“We continue to innovate on our proactive technology to detect fake reviews and other indications of unusual behaviour,” he added.

This is on the same line as the company’s ‘Rekognition Content Moderation’ system which it uses to review harmful images in product reviews. The system combines machine learning and human-in-the-loop review, starting with a 40% automated image decision and gradually improving. Some self-managed models were transitioned to Amazon Rekognition Detect Moderation API for better accuracy.

This migration streamlined architecture, reducing effort and costs. The accuracy of Rekognition Content Moderation decreased human review needs and expenses, yielding significant benefits for product review moderation.

Amazon is strategically incorporating artificial intelligence into its product offerings. Instead of emphasising prominent AI chatbots or imaging tools, the company is concentrating on services that enable developers to build their own generative AI tools using its AWS cloud infrastructure.

Recently, it partnered with Meta to launch its LLaMa 2 and run it on AWS. Although Amazon wouldn’t share the details on its AI/ML models, the “review summarisation” tool could well be based upon Meta’s LLaMa 70B model.

Earlier this year, Amazon’s CEO, Andy Jassy, said that generative AI holds significant implications for the company’s future. This is evidenced by the ongoing generative AI initiatives across Amazon’s various business units.

More Questions,

However, this recent announcement about using AI to combat fake reviews raises questions about potential bias in the summary generation process. While AI can condense vast amounts of information into summaries, there’s concern that Amazon’s profit motives might influence how the AI presents information. 

This could lead to favouring high-margin products and established brands, potentially causing disadvantages to small-size sellers with limited marketing budgets. 

Legal Action

Amazon’s customer reviews have been a vital part of its platform since 1995, therefore, it’s smart to keep improving their utility with AI. However, with the advent of fake review brokers, the review has lost credibility to a huge extent. Reports indicate that up to 40% of reviews on the platform are potentially fake.  

Amazon’s commitment to combating fake reviews is further demonstrated by its recent legal action against brokers suspected of promoting the creation of fraudulent Amazon reviews.

“Another way we fight fake reviews is through legal action. Not only are we targeting the source of the problem but we’re sending a clear message that there’s no place for abuse in our stores and we will hold fraudsters accountable,” said Montague.

The Federal Trade Commission also recently proposed a rule to ban deceptive online reviews, aiming to enhance credibility. The rule’s development, starting in 2019, has involved cases against misleading claims and fake reviews. The proposed rule prohibits selling or soliciting fake reviews, including fabricated profiles, AI-generated content, and reviews from non-users, with penalties for violations. 

Other prohibited activities include buying positive/negative reviews for any product, allowing reviews from leadership/affiliates without proper disclosure, operating review sites as “independent” for one’s products, suppressing reviews through threats/intimidation, and selling fake engagement metrics like followers and video views.

Scope for More

Amazon’s efforts have yielded results, as the company reported blocking over 200 million suspected fake reviews in the past year using these methods. The retail platform acknowledges that a collaborative approach involving private sector entities, consumer groups, and governments is crucial for effectively addressing the problem.

Despite Amazon’s endeavours, consumer groups believe that more needs to be done to combat the widespread issue of fake reviews. While Amazon’s use of AI and legal actions against fake review operators have shown progress, the consumer group emphasises the need for stronger legislative measures and further cooperation to ensure a genuine and trustworthy online shopping experience.