Detect AI-generated voice scams & alert your network
My role
Lead product designer, researcher, product manager
Skills & technologies
UX design, physical prototyping, multimodal design, haptics, wearables, mobile app design, visual design, design systems
Industries
AI, cybersecurity
Timeframe
10 weeks
Final mobile screens for Orbit
What is Orbit?
Orbit combats AI voice cloning scams by detecting voice anomalies, flagging suspicious activity, and alerting trusted contacts. It ensures digital security with customizable settings and incident reviews.
Phishing scam detection wearable device that syncs with mobile phones
AI-powered using a model such as Ircam Amplify’s AI Speech Detector Model
Target users: Retirees and their families, particularly adult children
Designing for the Near Future
Our assumptions about the nature of AI and of phishing scammers’ technology puts us about 5-10 years into the future.
We expect that AI will evolve to the point where scammers can use it to fake phone numbers or even display names and video calls, making scams harder to detect at a glance.
We assume that we will be able to integrate an existing AI model, such as Ircam Amplify’s AI Speech Detector Model, in order to detect voice cloning scams.
Goals
Understand how both older and younger adults navigate technology to detect phishing scams, addressing a growing concern in cybersecurity
Target users
Younger adults (millennials): How do younger generations interact with this tech?
Adults 65 and up: Iterated design based on feedback from the younger generation. How does this group interact with it?
Methods
7 semi-structured in-depth interviews
Social media polling
Key Insights from Semi-Structured Interviews
Building Trust Through Familiarity
Scammers leverage the principle of “familiarity” to establish a sense of trust, making their scams appear more credible and convincing to the victim.
Creating Panic Through Urgency
Scammers exploit “urgency” to create a sense of panic, pressuring victims to act quickly without questioning the legitimacy of the situation.
Exploiting Low Tech Literacy
Scammers take advantage of lower tech literacy to confuse and mislead victims, making it harder for them to recognize and avoid scams.
Concept Development & Ideation
During the concept development phase, I led our group through a series of brainstorming and sketching exercises to generate and explore a wide range of ideas.
I helped facilitate decision-making and guided the team in narrowing our concepts down to three key directions, which we then presented for feedback. This set us up to identify our core design challenge.
Design Challenge
How might we leverage trust within families, across varied levels of tech literacy, to create a shared learning experience that helps everyone recognize and avoid AI voice cloning phishing scams?
Building the Prototype
The key flows we wanted to implement in our prototype were:
Detecting AI voice cloning scams
Sharing scam information with family
Reviewing scam details and checking in with family
We built a couple versions of a testable prototype: a bracelet with 3 different LED colors that signify whether or not an AI scam is present.
Scanning call in progress = yellow
Safe call = green
AI detected = red + vibration
I built the physical shell for our device, while others on my team programmed the LED functioning.
I also wrote the user testing protocol for our next round of research, in preparation for our visit to a retirement community to interview folks and get feedback on our prototype.
Evaluative Research & Insights
We completed multiple sets of user testing sessions, 4 of them with younger adults, and after adjusting our prototype based on feedback from those tests, we tested the updated version with 6 adults over the age of 65.
The key flows we evaluated in this prototype were:
Detecting AI voice cloning scams
Sharing scam information with family
Reviewing scam details and checking in with family
What did we learn?
Users want to understand how to report scams to the proper authorities.
A key element of feeling protected against scams includes open communication and sharing information with family and friends.
Wearing a smart bracelet helps users detect scams even when they aren’t looking at their phone.
Final Design
A Multimodal Experience
To make scam detection intuitive and non-intrusive, we designed a speculative multi-touchpoint system that included a wearable bracelet, mobile interfaces, and family notifications.
Color-Coded Alerts
Yellow: Potential risk detected. User is prompted to slow down and review.
Green: Safe! No action needed.
Red: High probability of an AI scam. Alerts are triggered, and a review is prompted.
Scam Review in the Mobile App
When the bracelet detects suspicious activity, the user is guided to a review screen on their phone. The interface is designed for clarity and quick comprehension:
Contextual summary of the suspicious activity
Clear “Proceed” or “Report” options
Accessible language tailored for older adults
Alert Family Members
If a high-risk interaction is detected, a designated family member receives a real-time notification with options to follow up or offer support. For example, if Sam encounters a phishing call, his son receives an alert with suggested next steps.
Reflection
Successes
Cross-generational testing helped us collect a range of opinions on our device
Physical prototype engagement helped us conduct effective user tests
Our excellent group dynamic allowed for effective team collaboration
Challenges
Our ability to implement this tech right now is limited by existing AI capabilities, and by the fact that we have to speculate about future states of phishing scams
The success of this device relies heavily on healthy relationships and communication between family members
Looking Ahead
Broaden scam detection beyond voice cloning; we’re curious to explore how our product can evolve and address broader challenges in the future
Evaluate haptic feedback and incident reporting in more depth