Demographic Bias Correction via Weighted Data Structure
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Solution Overview
Problem
Existing audience measurement systems face inefficiencies in correcting for bias in ratings data due to interdependencies among demographic statistics, requiring multiple processing iterations to adjust scale factors and often exacerbating biases between related demographic categories.
Innovation Solution
The implementation of a method that determines a set of weights to reduce bias across multiple demographic statistics in a single processing pass, eliminating the need for iterative adjustments and improving processor efficiency by adjusting demographic attributes based on these weights to generate accurate ratings data for the target population.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple processing iterations are used to adjust scale factors for correcting bias in ratings data, then measurement precision may be improved, but processing time and computational complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing baseline demographic statistics for various population groups before processing ratings data. These pre-computed baselines are then directly compared with observed demographic statistics from the ratings data, allowing for immediate bias identification and correction without requiring multiple iterative processing passes. This eliminates the time-consuming iterative adjustments while maintaining measurement precision.
2Measurement precision
If iterative adjustments of scale factors are performed to correct bias, then demographic accuracy may improve, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the population into distinct demographic groups (e.g., age groups, gender, ethnicity) and calculates separate baseline statistics for each segment. This segmentation allows for independent comparison and correction of bias for each demographic group without requiring complex iterative adjustments across the entire population. The segmentation simplifies the processing system by breaking down the complex problem into manageable, independent calculations.
Solution Approach 2:
The patent introduces baseline demographic statistics as an intermediary reference that mediates between the observed ratings data and the target population characteristics. These baselines serve as a pre-computed reference framework that simplifies the correction process by providing direct comparison points, eliminating the need for complex iterative scale factor adjustments and reducing overall system complexity.
3Measurement precision
If iterative processing is used to adjust scale factors, then bias correction may be improved, but productivity decreases due to multiple processing passes
Solution Approach 1:
The patent performs preliminary calculations of baseline demographic statistics and expected population distributions before processing the actual ratings data. This preliminary action creates a ready-reference framework that enables direct, single-pass comparison and correction, eliminating the need for multiple iterative processing passes and significantly improving processing efficiency while maintaining bias correction accuracy.
4Measurement precision
If scale factors are adjusted through multiple iterations, then demographic representation may improve, but computational resources are consumed excessively
Solution Approach 1:
The patent segments demographic analysis into independent group calculations with pre-computed baselines, allowing for efficient parallel processing and reducing overall computational resource consumption. By dividing the population into discrete segments with independent baseline statistics, the system avoids the excessive computational requirements of iterative full-population adjustments while maintaining accurate demographic representation.
Data Source
AI summary
Examples apparatus disclosed herein are to determine a plurality of weights based on a data structure having elements corresponding to pairings of ones of a plurality of demographic partition statistics and ones of a plurality of baseline demographic statistics obtained for a target population, the demographic partition statistics corresponding to a plurality of demographic partitions of a sample population, a first element of the data structure to combine a first one of the demographic partition statistics with a first one of the baseline demographic statistics of the target population based on a first value corresponding to a numerator term of an expression and a second value corresponding to a denominator term of the expression, the weights corresponding respectively to the demographic partitions of the sample population. Disclosed example apparatus are also to adjust the attribute data based on the weights to determine ratings data for the target population.