Engineering trust in product reviews with human-AI synergy
Every day, millions of shoppers rely on Amazon reviews to make purchase decisions. Yet in 2022 alone, over 200 million fake reviews slipped through even advanced AI detection systems. This erosion of trust doesn’t just hurt buyers; it undermines sellers and Amazon’s marketplace integrity.
As design lead for Amazon’s 24-hour Industry Project, I confronted this challenge head-on: How might we verify review authenticity at scale without compromising user privacy?
Our solution fused human expertise with LLM intelligence to create a new standard for trustworthy reviews. By engineering privacy-conscious validation methods and retraining detection models with Vine Voice feedback, we gave shoppers something priceless: confidence that every "helpful" tag reflected genuine quality, not manipulation.
Achievements
Won 1st place for designing the best solution to ensure trust and transparency across the platform.
Role
UX Designer
Skills
Product Design
Problem Solving
Branding
Prototyping
Timeline
September 2024 (24 hours)
The Challenge
Today, finding a trustworthy review means detective work
Amazon.com hosts over one billion reviews, playing a critical role in online shopping by shaping purchasing decisions and product success. Yet fake reviews and unexplained low ratings undermine trust, making it harder for shoppers to rely on feedback.
The core challenge: How can we ensure authentic, high-quality reviews to maintain trust between buyers and sellers?
Example of disparity in Amazon review quality.
Current Landscape
How does Amazon keep reviews real—and where does it fall short?
Amazon requires all reviewers to log in (with 2FA/passkeys) and verify purchases, while its invite-only Vine Voice program adds an extra layer of credibility through pre-vetted users.
While these measures ensure reviews come from real humans, they don't address incentivizing high-quality feedback from regular users and helping consumers quickly identify trustworthy reviews.
Know the User
Thoughtful shoppers who share authentic product experiences to help others make better decisions.
Goals
Strengthen review credibility by incentivizing and rewarding high-quality human feedback at scale.
Approach
By applying Vine Voice’s verification methods and reputation tools, we can improve trust in all reviews.
Synthesis
A unique opportunity for Vine Voices
Facing limited Vine Voice access, we reverse-engineered review trust systems through competitive analysis. The insight? No platform combined expert validation, consensus checks, and incentives…until now.
Our solution transforms Vine Voices into certifiers of quality, not just contributors.
User Journey
We created Amira, a seasoned Vine Voice member, to define the ideal user journey. Her ability to spot authentic, high-quality feedback guides our system design—ensuring rigorous review verification and rewarding valuable contributions.
Explorations
Designing an Amazon-native review experience
Maintaining Amazon’s signature look and feel was critical. We anchored our designs in the existing product review experience to ensure seamless familiarity. By replicating interaction patterns and visual hierarchies from Amazon’s public review system, we preserved the platform’s native UX language.
Key Features
Feature #1: Product review verification dashboard
Our team realized Vine Voices needed validation tools that matched how they naturally evaluate reviews—through quick binary ratings followed by written reasoning. By designing around this rhythm, we created a system that could produce rich data for Amazon’s algorithms.
Feature #2: No bots allowed, verified humans only
While Amazon allows anyone to browse without logging in, verified users bring trust and credibility to the platform. These users are authenticated through multi-factor authentication, ensuring they’re real humans.
Feature #3: Rewarding quality contributions
We incentivized Vine Voices with instant rewards (Amazon credits per review) and lasting recognition (exclusive badges), ensuring their efforts feel valued while improving review quality and AI training.
The Impact
Boosting Amazon’s trust through verified review accuracy
This initiative supports Amazon’s core goal of maintaining review integrity to boost customer trust. When shoppers can rely on accurate reviews, they make confident purchases and keep returning to Amazon. By creating Vine’s verified review accuracy system, I ensured helpful labels truly reflected quality, not just popularity.
With just 24 hours and no Vine access, we moved fast: reverse-engineering Amazon’s review system while adapting Human-in-the-Loop best practices from other platforms. Through rapid prototyping and tight developer collaboration, we balanced user trust with technical constraints—proving even a time-boxed sprint could deliver a scalable trust framework.
Design
Isanna Wong
Yasin Mete
Data
Somayyeh Gholizadeh
Crystal Gil Herrera
Nicole Hixon
Engineering
Sung Kim
Linh Pham
Angelo Yap
Cybersecurity
Isabelle Jaber