Bias Identification NLP Using Entity Context Spans
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Solution Overview
Problem
Existing computer systems struggle to automatically recognize and address bias in text, which can lead to discomfort for readers due to unconscious or conscious injection of bias by drafters.
Innovation Solution
Implementing machine-learning algorithms and neural networks to analyze text for bias, including feature extraction and classification, with engines configured to identify and mitigate bias in real-time, and provide suggestions for neutral language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning algorithms and neural networks are implemented to analyze text for bias, then bias identification capability is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of bias detection into multiple specialized engines: a natural language processing engine that tokenizes text and identifies entities/attributes, and a bias determination engine that analyzes contextual usage. This segmentation allows each component to focus on specific aspects of bias detection, improving overall precision while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary entity classifier that bridges the gap between raw text tokens and bias analysis. The entity classifier identifies and categorizes entities and attributes within text, serving as a mediator that transforms unstructured text into structured data that the bias determination engine can effectively analyze, thereby improving measurement precision without directly increasing the complexity of the core bias detection logic.
2Speed
If real-time bias analysis is performed on text, then bias mitigation speed is improved, but use of energy increases
Solution Approach 1:
The system applies partial action by focusing bias analysis on specific entities and attributes identified by the entity classifier, rather than analyzing entire texts uniformly. The bias determination engine concentrates computational resources on tokens and spans that are likely to contain bias based on entity type and contextual cues, reducing overall energy consumption while maintaining real-time detection capability for critical bias instances.
Solution Approach 2:
The patent implements local quality by applying different analysis depths to different portions of text based on their bias risk. High-risk entities and attributes receive intensive bias analysis, while low-risk portions receive minimal processing. This localized approach to quality control enables real-time bias mitigation speed by prioritizing computational energy on the most critical segments of text.
3Measurement precision
If comprehensive entity and attribute classification is performed, then bias detection accuracy is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The entity classifier segments the complex task of text analysis into distinct classification stages: token-level entity recognition, attribute identification, and contextual relationship analysis. Each segmentation step produces intermediate results that build toward comprehensive bias detection accuracy, while the modular structure reduces the overall difficulty by breaking down the measurement challenge into manageable components.
Solution Approach 2:
The system employs feedback mechanisms where the entity classifier uses information from previous classification stages to inform subsequent analysis. The bias determination engine receives structured entity and attribute data from the classifier, and this feedback loop allows the system to iteratively refine its detection accuracy. The feedback architecture improves measurement precision by allowing each component to learn from and adjust based on the outputs of other components, reducing the overall difficulty of comprehensive detection.
Data Source
AI summary
A computing machine accesses text from a record. The computing machine identifies, using a natural language processing engine, an entity mapped to a first span of the text. The first span includes a contiguous sequence of one or more words or subwords in the text. The computing machine determines a bias category for the entity. The bias category is selected from a predefined list of bias categories. The determined bias category for the entity depends on a second span of the text. The second span includes a contiguous sequence of one or more words or subwords in the text. The second span is different from the first span.