Item logo image for BiasRadar – Reveal Algorithm Bias

BiasRadar – Reveal Algorithm Bias

biasradar.ai
4.9(

14 ratings

)
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Overview

Premium integrated co-browsing for BiasRadar. Securely share your browsing session with collaborators.

BiasRadar – Understand Personalization & Compare Online Experiences BiasRadar is a transparency-focused browser extension designed to help users understand how personalization can influence what they see online, including pricing, search results, and recommendations. Modern digital platforms often tailor content based on factors like location, browsing behavior, and account preferences. This means that different users may see different versions of the same page. BiasRadar helps make these differences easier to observe and compare. What BiasRadar Does • Highlights potential variations in pricing across sessions • Helps compare differences in search results and rankings • Identifies patterns in personalized content and recommendations • Enables side-by-side session comparison for analysis • Supports collaborative review across multiple users (optional) Why It’s Useful Personalization can affect: • Product prices and offers • Travel fares and availability • Shopping results and recommendations • News feeds and content ordering • Advertisements and promotions BiasRadar provides visibility into these variations so users can make more informed decisions while browsing. Practical & Everyday Use Cases Comparing Prices Across Sessions Users can check the same product or service across different sessions (for example, different devices or accounts) to observe whether pricing or offers vary. Exploring Location-Based Differences By comparing sessions from different regions or network conditions, users can understand how location may influence pricing or availability. Logged-In vs Logged-Out Views Users can compare how content appears when signed in versus browsing anonymously, helping them better understand personalization effects. Evaluating Offers and Recommendations BiasRadar helps users observe how product listings, discounts, or delivery options may differ across sessions. Understanding Content Ordering Users can explore how search results or listings may be arranged differently based on personalization factors. Who It’s For • Consumers interested in comparing online experiences • Researchers studying personalization patterns • Analysts and auditors reviewing digital systems • Journalists exploring how content is presented online • Developers and product teams evaluating user experience Privacy-Focused Design BiasRadar is built with user privacy in mind: • No selling of personal data • User-controlled activation • Transparent permissions • Session-based comparison without unnecessary data collection Users remain in control of how and when the extension is used. Key Use Cases • Compare pricing across sessions • Explore differences in content and rankings • Understand personalization patterns • Analyze variations in recommendations • Support research and documentation Note BiasRadar is designed for transparency and informational purposes. It does not modify website behavior or interfere with platform functionality.

Details

  • Version
    1.0
  • Updated
    March 19, 2026
  • Size
    27.79KiB
  • Languages
    English
  • Developer
    Website
    Email
    steve@algorethics.ai
  • Non-trader
    This developer has not identified itself as a trader. For consumers in the European Union, please note that consumer rights do not apply to contracts between you and this developer.

Privacy

Manage extensions and learn how they're being used in your organization

BiasRadar – Reveal Algorithm Bias has disclosed the following information regarding the collection and usage of your data. More detailed information can be found in the developer's privacy policy.

BiasRadar – Reveal Algorithm Bias handles the following:

Web history
User activity
Website content

This developer declares that your data is

  • Not being sold to third parties, outside of the approved use cases
  • Not being used or transferred for purposes that are unrelated to the item's core functionality
  • Not being used or transferred to determine creditworthiness or for lending purposes

Support

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