Browser Dark Pattern Detection With Neural Risk Scoring
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Solution Overview
Problem
Conventional web browsers lack the technical ability to detect and counter dark patterns in user interface designs that manipulate users into making decisions against their best interests, as ad blockers primarily target graphical elements like ads and miss these manipulative designs.
Innovation Solution
A neural network, such as a convolutional neural network (CNN), is trained to calculate risk scores for webpage elements, identifying potential dark patterns, and executes mitigation actions like blocking, modifying, or notifying users about these elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ad blockers are used to block webpage elements, then graphical ads are blocked, but manipulative dark patterns are missed
Solution Approach 1:
The patent replaces conventional mechanical ad-blocking methods with a neural network-based detection system. The neural network analyzes webpage elements to identify dark patterns based on their functional behavior and contextual relationships, rather than relying on graphical recognition or predefined ad signatures. This substitution enables detection of non-graphical manipulative elements while maintaining browser simplicity.
Solution Approach 2:
The patent introduces a neural network as an intermediary component between the browser and webpage elements. This intermediary layer analyzes the relationships and contextual features of webpage elements to identify dark patterns, serving as a mediator that enhances detection capability without directly modifying the core browser architecture or user interface.
2Reliability
If neural network analysis is performed on all webpage elements, then dark pattern detection improves, but processing time increases
Solution Approach 1:
The patent segments the webpage into distinct elements and analyzes them individually through the neural network. By processing elements separately rather than analyzing the entire page at once, the system reduces computational burden and processing time while maintaining comprehensive detection coverage. Each element is evaluated for its inherent dark pattern characteristics independent of other elements.
Solution Approach 2:
The patent applies partial analysis by focusing the neural network on specific elements that exhibit characteristics suggestive of dark patterns, rather than uniformly analyzing all elements. This selective approach reduces overall processing time while maintaining high detection accuracy for the most problematic elements.
Data Source
AI summary
A user is detected navigating to a webpage. A plurality of web elements of the webpage is identified. One or more web elements is determined to have a risk score that surpasses a risk threshold of being a dark pattern. The risk score is identified by analyzing each of the plurality of web elements. A mitigation action configured to counteract the dark pattern is executed in response to the one or more web elements having the risk score that surpasses the risk threshold. The mitigating action is executed substantially immediately upon the user navigating to the webpage.