Bias Mitigation in Augmented Reality Sessions
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Solution Overview
Problem
Users in virtual and augmented reality experiences often encounter biases from other users, leading to negative interactions and premature session termination, as existing technologies lack mechanisms to effectively mitigate these biases before or during digital reality sessions.
Innovation Solution
A system and method that analyze social media metadata to identify user biases, generate heat maps, and modify the digital reality session display by applying masked overlays or filters to minimize negative interactions, thereby enhancing the user experience and increasing session duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If users engage in shared digital reality sessions without bias mitigation, then social interaction authenticity is maintained, but negative interactions increase and session duration decreases
Solution Approach 1:
The system performs preliminary analysis of user profile data, social media metadata, and historical interaction patterns before the digital reality session begins. This preliminary action identifies potential biases and generates mitigation strategies in advance, allowing the system to proactively prevent negative interactions rather than reacting to them during the session, thereby extending session duration while reducing harmful factors.
Solution Approach 2:
The system introduces an intermediary layer between users in the digital reality session that filters and moderates interactions based on analyzed bias profiles. This intermediary mechanism selectively adjusts content visibility, interaction permissions, and communication channels to prevent biased users from engaging in negative interactions, thus maintaining session longevity without completely isolating users from each other.
2Reliability
If bias analysis and mitigation mechanisms are implemented, then negative interactions are reduced, but system complexity increases
Solution Approach 1:
The system employs multi-functional algorithms that perform multiple tasks simultaneously: analyzing user profiles, detecting biases, generating heat maps, and creating mitigation strategies all within a single integrated processing framework. This universal approach reduces system complexity by eliminating the need for separate specialized modules for each function, while still maintaining high interaction quality through comprehensive bias mitigation.
Solution Approach 2:
The system dynamically adjusts mitigation parameters based on the severity and type of detected biases rather than applying fixed complex rules to all situations. By changing parameters such as filter intensity, content visibility thresholds, and interaction restrictions based on real-time analysis results, the system achieves reliable interaction quality with adaptable complexity that scales with the actual bias levels present in each session.
3Reliability
If comprehensive bias analysis is performed on all users, then session compatibility is improved, but processing time increases
Solution Approach 1:
The system performs comprehensive bias analysis selectively rather than uniformly on all users. It prioritizes in-depth analysis for users with higher-risk profiles based on preliminary screening, while applying streamlined analysis to low-risk users. This partial action approach maintains session compatibility by thoroughly analyzing those who need it most, while reducing overall processing time by not applying the same level of analysis to all participants.
Solution Approach 2:
The system conducts preliminary bias analysis and generates initial compatibility assessments before users join the digital reality session. This preliminary action creates a foundation for rapid ongoing assessments during the session, allowing the system to maintain high session compatibility through continuous monitoring without performing full comprehensive analysis repeatedly, thus minimizing processing time while ensuring compatibility.
Data Source
AI summary
Embodiments of the present invention disclose a method, computer program product, and system for identifying biases of one or more users during a shared augmented reality session and modifying the display. A first login is detected of an augmented reality. A first set of biases associated with the first user are generated. Visual data is received from a device associated with the first user profile. A masked overlay for display is generated and displayed to the first user based on the received visual data and the first set of biases. A second login of the augmented reality session is received. A second set of biases associated with the second login is generated. In response to analyzing a generated heat map, a positivity score is calculated. Changes are monitored for, in the first set of biases and second set of biases. The masked overlay is displayed.