The Price of Prestige: Are Premium Phones a Target for Exploitation?

In recent years, allegations have surfaced that several apps and platforms, including those for shopping, flight bookings, and hotel reservations, may charge users differently based on the perceived value of their devices. This means that individuals using premium smartphones, often priced above ₹30,000, may unknowingly pay more for the same services than those using less expensive devices. This practice, though not explicitly confirmed by most companies, raises significant ethical, legal, and technological questions.

How Does This Work?

Apps can infer the value of a device by accessing details such as:

1. Device Model and Manufacturer: By identifying whether a user is on a flagship device like an iPhone or Samsung Galaxy S series, platforms can gauge the user’s purchasing power.
2. Operating System Version: Premium devices often run the latest OS updates.
3. Browser User-Agent Strings: Websites can identify the type of device and use this information to adjust pricing dynamically.
4. Device ID or Advertising ID: Platforms can correlate data across apps and websites to profile a user’s spending habits.

Such information is often used under the guise of “personalization,” but it can be misused for differential pricing.

How Apps Might Fetch Device Information

Below is a demonstration of how apps can collect device details without requiring special permissions. Using these details, platforms could infer the price of a device and use this information to dynamically adjust pricing.

Android Example (Kotlin)

import android.os.Build

fun getDeviceDetails(): Map<String, String> {
    val details = mapOf(
        "Brand" to Build.BRAND,      // e.g., "Samsung"
        "Model" to Build.MODEL,      // e.g., "Galaxy S23"
        "Manufacturer" to Build.MANUFACTURER, // e.g., "Samsung"
        "OS Version" to Build.VERSION.RELEASE // e.g., "13"
    )
    return details
}

// Example usage
fun main() {
    val deviceDetails = getDeviceDetails()
    println("Device Details: $deviceDetails")

    // Mock database lookup for price inference
    val price = fetchDevicePrice(deviceDetails["Brand"], deviceDetails["Model"])
    println("Estimated Price: ₹$price")
}

fun fetchDevicePrice(brand: String?, model: String?): Int {
    // Simulated device price lookup
    val priceDatabase = mapOf(
        "Samsung Galaxy S23" to 70000,
        "iPhone 14" to 80000,
        "Redmi Note 12" to 18000
    )
    return priceDatabase["$brand $model"] ?: 0
}

iOS Example (Swift)

import UIKit

func getDeviceDetails() -> [String: String] {
    let device = UIDevice.current
    return [
        "Brand": "Apple",            // Apple devices only
        "Model": device.model,       // e.g., "iPhone"
        "System Name": device.systemName, // e.g., "iOS"
        "System Version": device.systemVersion // e.g., "17.0"
    ]
}

// Example usage
func main() {
    let deviceDetails = getDeviceDetails()
    print("Device Details: \(deviceDetails)")

    // Mock database lookup for price inference
    let price = fetchDevicePrice(brand: deviceDetails["Brand"], model: deviceDetails["Model"])
    print("Estimated Price: ₹\(price)")
}

func fetchDevicePrice(brand: String?, model: String?) -> Int {
    // Simulated device price lookup
    let priceDatabase: [String: Int] = [
        "Apple iPhone 14": 80000,
        "Apple iPhone SE": 40000,
        "Apple iPhone 11": 30000
    ]
    return priceDatabase["\(brand ?? "") \(model ?? "")"] ?? 0
}

These examples demonstrate how apps can identify the device model and cross-reference it with a database of device prices. Since these methods do not require explicit permissions, users are often unaware that their device details are being collected and used in this way.

Industries Implicated

1. E-commerce Platforms: Reports suggest users may see higher prices for products when accessing websites from premium devices.
2. Flight and Hotel Bookings: Platforms may use device-based profiling to increase prices for users on premium devices, assuming they are less price-sensitive.
3. Subscription Services: Premium devices are often targeted with higher subscription fees under the assumption that users can afford to pay more.

Ethical and Legal Concerns

1. Discrimination: Charging users differently for the same product or service based solely on their device type is a form of economic discrimination.
2. Transparency: Most users are unaware that their device could influence pricing, violating principles of informed consent.
3. Data Privacy: Collecting device information to infer spending capacity often happens without explicit user permission, raising privacy concerns.
4. Consumer Trust: Differential pricing can erode trust in platforms, especially if users feel exploited based on their choice of device.

Should Governments Impose Fines?

Governments should consider imposing fines or regulations to ensure:

1. Transparent Pricing Models: Platforms must disclose when and how pricing varies based on user data.
2. Limits on Data Use: Clear guidelines on what device data can be used for pricing decisions.
3. Consumer Remedies: Mechanisms for users to report and seek redress for unfair pricing.

For instance, the European Union’s Digital Services Act emphasizes transparency in online transactions, providing a potential model for global regulations.

Role of Phone Companies

Tech giants like Apple and Google could take proactive steps:

1. Terms of Service (ToS): Enforce rules preventing apps from using device information for unfair pricing.
2. Privacy Controls: Enhance settings that allow users to block apps from accessing device-specific information.
3. App Store Policies: Ban apps that engage in discriminatory pricing practices from their ecosystems.

Apple’s App Tracking Transparency initiative is a step in the right direction, but it must extend to cover device-based profiling for pricing purposes.

The Way Forward

To address this issue, a multi-stakeholder approach is essential:

1. Governments must establish legal frameworks for fair pricing.
2. Tech Companies should implement stricter data privacy measures.
3. Consumers should demand transparency and advocate for their rights.

Conclusion

The practice of charging users based on their device’s perceived value undermines trust, fairness, and transparency in digital transactions. While dynamic pricing can optimize revenue for businesses, its misuse to exploit certain user segments is unethical and must be addressed. Governments, tech companies, and consumers all have a role to play in ensuring fairness in the digital economy. Without intervention, this practice risks exacerbating inequality and further eroding trust in technology.

Would you continue using an app if it charged you more just because of your phone? The time to question and challenge such practices is now.