Sentiment Analysis for Bias Detection in Edited Public Safety Video
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Solution Overview
Problem
Public safety incidents captured on video often require editing to exclude sensitive content, leading to potential bias in the released footage, which can be perceived as favoring one party over the other, causing suspicion and mistrust when delays in video release occur.
Innovation Solution
A method and system that compute sentiment scores for both unedited and edited videos, comparing them to detect bias by segmenting the videos into fragments based on behavioral analytics, providing suggestions to ensure the edited video maintains the original sentiment, thereby reducing bias and facilitating transparent video release.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If video is edited to exclude sensitive content, then public safety concerns are addressed, but bias is introduced that favors one party over the other
Solution Approach 1:
The video is divided into multiple clips based on detected events and their sentiment scores. By segmenting the video at event boundaries rather than arbitrarily cutting sensitive portions, the system maintains the integrity of each event while allowing selective exclusion of entire clips that contain sensitive content. This segmentation approach preserves the authenticity of released clips since they represent complete, unmodified event segments.
Solution Approach 2:
An automated sentiment analysis system acts as an intermediary between the raw video footage and the final released version. This intermediary objectively identifies events, assigns sentiment scores, and determines which clips to exclude based on predefined criteria, removing human bias from the editing decision-making process. The system mediates between the need to protect sensitive content and the need to maintain video authenticity by using algorithmic rather than subjective judgment.
2Object-affected harmful factors
If video release is delayed for editing, then sensitive content can be protected, but public suspicion and mistrust increase
Solution Approach 1:
The system performs preliminary automated editing by detecting events, assigning sentiment scores, and identifying clips to exclude before human review. This preliminary action prepares the video for rapid release by pre-processing the content according to established criteria, allowing minimal delay between incident occurrence and video release while still protecting sensitive content through automated clip selection.
Solution Approach 2:
The system changes the parameter of video processing from manual frame-by-frame review to automated event-based clip selection. By transforming the editing approach from continuous manual analysis to discrete automated event detection and clip assembly, the system dramatically reduces processing time while maintaining appropriate content protection through algorithmic sentiment analysis and event boundary detection.
3Object-affected harmful factors
If manual video editing is performed, then sensitive content can be selectively removed, but human bias influences which content is excluded
Solution Approach 1:
The video editing system performs self-service through automated sentiment analysis and event detection algorithms that objectively identify and score events without human intervention. The system independently determines which clips contain sensitive content based on predefined sentiment thresholds and event types, eliminating the need for subjective human judgment in the editing decision-making process while maintaining consistent application of content protection criteria.
Solution Approach 2:
The patent replaces the mechanical process of manual video review and editing with an automated computational system that uses sentiment analysis and event detection algorithms. This substitution transforms the editing process from a human-dependent, subjective mechanical operation to an automated, objective computational process that consistently applies predefined criteria across all video content without human bias or variability.
Data Source
AI summary
Techniques for eliminating bias in selectively edited videos are provided. A request to release a video capturing a public safety incident is received. The video is edited to create an edited video. At least one civilian score and at least one public safety official score based on the sentiment of the video is computed. At least one edited civilian score and at least one edited public safety official score based on the sentiment of the video is computed. A first score is computed based on a combination of the civilian score and public safety official score. A second score is computed based on a combination of the edited civilian score and edited public safety official score. The first and second score are compared to determine if a difference between the scores exceed a threshold. The edited video is released when the scores do not exceed the threshold.