Adversarial Language Imitation for Bias-Aware Toxicity Detection
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Solution Overview
Problem
Toxic language detection systems often misclassify statements as toxic or non-toxic based on minority group mentions, leading to underdetection of subtle hate and over-detection of benign content, exacerbating the marginalization of minority groups.
Innovation Solution
An adversarial classifier is used in a loop with a pre-trained language model to generate phrases that confuse traditional classifiers, balancing classification scores and probabilities to create nuanced training data that improves the accuracy of toxicity detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the language classifier relies on minority group mentions for toxicity detection, then the detection of explicit hate speech is improved, but the false flagging of benign content increases and minority groups are marginalized
Solution Approach 1:
The patent implements an adversarial framework where a language model generates phrases and a classifier evaluates them, with the results fed back to refine the classifier. This feedback loop allows the system to learn from its mistakes and reduce false flagging of benign content while maintaining toxicity detection accuracy.
Solution Approach 2:
The patent introduces a language model as an intermediary between the input text and the toxicity classifier. This intermediary generates variations and explanations that help the classifier understand the semantic meaning of phrases, enabling it to distinguish between benign content mentioning minority groups and actually toxic content.
2Device complexity
If the language classifier uses traditional classification methods, then the system is simple to implement, but it cannot distinguish between implicitly toxic and benign language
Solution Approach 1:
The patent embeds multiple processing layers within each other: the language model generates phrases and explanations nested within the classification process, which itself is nested within the adversarial training framework. This nested structure enables sophisticated implicit toxicity detection while managing complexity through modular organization.
Solution Approach 2:
The patent adds new dimensions to the classification problem by generating not only the phrase itself but also alternative phrasings and semantic explanations. This dimensional expansion allows the classifier to analyze language from multiple perspectives, improving its ability to detect implicit toxicity without significantly increasing implementation complexity.
3Measurement precision
If the system generates more training data to improve classification accuracy, then the detection performance improves, but the time and computational resources required increase
Solution Approach 1:
The patent implements a self-service training mechanism where the language model generates its own training data by creating phrases and evaluating them through the adversarial process. This self-generated training data eliminates the need for manual data collection and annotation, significantly reducing the time and computational resources required while improving classification accuracy.
Solution Approach 2:
The patent establishes a continuous loop where the classifier trains on generated data, evaluates new phrases, and feeds results back to generate improved training data. This continuous process ensures that the system constantly improves its accuracy without requiring discrete, time-consuming training phases, thereby reducing overall training time while maintaining high performance.
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for generating a phrase that is confusing to a language classifier. A method can include determining, by the LC, a first classification score (CS) of a prompt indicating whether the prompt is a first class or a second class, predicting, based on the prompt and by a pre-trained language model (PLM), likely next words and a corresponding probability for each of the likely next words, determining, by the LC, a second CS for each of the likely next words, determining, by an adversarial classifier, respective scores for each of the likely next words, the respective scores determined based on the first CS of the prompt, the second CS of the likely next words, and the probabilities of the likely next words, and selecting, by an adversarial classifier, a next word of the likely next words based on the respective scores.