Video Analysis Feedback System Resolving Opaque Classification
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Solution Overview
Problem
Automated video analysis systems for evaluating job applicants are often biased and opaque, making it difficult for applicants to understand the criteria used for video assessments and predict their performance, especially when access to third-party systems is limited.
Innovation Solution
A machine learning system that trains models to classify video data using a generative adversarial network (GAN) framework, allowing users to record and analyze videos, providing feedback on video characteristics that influence classification results, and aligning with third-party systems without direct access, using self-reported ratings and objective outcomes to calibrate its output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated video analysis systems are used to evaluate job applicants, then evaluation efficiency is improved, but the systems become biased and opaque making it difficult for applicants to understand classification criteria
Solution Approach 1:
The system provides feedback to applicants by identifying specific video characteristics (visual, vocal, verbal) that influenced the classification result, and suggesting actionable changes to improve future video performance. This resolves the opacity issue while maintaining automated evaluation efficiency.
Solution Approach 2:
The system segments the video analysis into multiple independent characteristic categories (visual characteristics, vocal characteristics, verbal characteristics) with specific metrics for each. This segmentation allows the system to provide detailed, transparent feedback on specific aspects of video performance without compromising overall evaluation efficiency.
2Measurement precision
If third-party video analysis systems are used, then classification accuracy is improved, but accessibility is reduced as applicants cannot directly access or understand the systems
Solution Approach 1:
The system acts as an intermediary that replicates third-party classification behavior while providing full transparency and accessibility to applicants. It learns to predict third-party outcomes and presents this information in an understandable format with actionable feedback, maintaining accuracy while improving accessibility.
Solution Approach 2:
The system creates a copy of the third-party system's classification behavior by training on correlated data and outcomes, then uses this copied behavior to provide predictions and feedback to applicants. This allows applicants to access the classification logic without needing direct access to the proprietary third-party system.
3Reliability
If machine learning models are trained to predict third-party system classifications, then predictability of results is improved, but training data availability is reduced when direct access to third-party systems is limited
Solution Approach 1:
The system uses an intermediary approach by collecting training data through correlated sources (user-reported ratings, outcomes from video submissions) rather than direct access to third-party systems. This intermediary data collection method enables model training while maintaining predictability of classification results.
Solution Approach 2:
The system performs preliminary actions by collecting and storing training data (video characteristics and correlated outcomes) in advance through various channels before formal model training. This preliminary data accumulation ensures sufficient training data availability even without direct third-party system access.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for machine learning for video analysis and feedback. In some implementations, a machine learning model is trained to classify videos into performance level classifications based on characteristics of image data and audio data in the videos. Video data captured by a device of a user following a prompt that the device provides to the user is received. A set of feature values that describe audio and video characteristics of the video data are determined. The set of feature values are provided as input to the trained machine learning model to generate output that classifies the video data with respect to the performance level classifications. A user interface of the device is updated based on the performance level classification for the video data.