AI Bias Neutralization in Chatbot Response Generation
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Solution Overview
Problem
Automated conversation exchange software programs, such as chatbots, often unintentionally introduce human biases into conversations, leading to issues with users due to unaddressed linguistic nuances and context, particularly concerning protected attributes like nationality.
Innovation Solution
The method involves using AI techniques to identify biased words in chatbot responses and replace them with alternate words, employing AI models like GPT-2 and BERT to neutralize biases and provide unbiased conversation responses by processing conversation data and updating the AI models to recognize and mitigate discrimination in specific domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If automated conversation exchange software programs are designed by human actors, then the programs can be created and deployed, but they introduce human biases into conversations
Solution Approach 1:
The patent introduces an intermediary AI system that sits between the human-designed chatbot and the conversation flow. This intermediary automatically detects biased language patterns in real-time and replaces them with neutral alternatives, thereby mediating the harmful effect of human bias while preserving the chatbot's functionality.
Solution Approach 2:
The patent converts the harmful effect of biased language into a benefit by using the same AI technology that generated the bias to detect and correct it. The system learns from biased patterns and transforms them into opportunities for improving conversation fairness and inclusivity through automatic replacement with neutral language.
2Productivity
If chatbots drive conversations towards protected attribute-specific bias, then the conversation flow is simplified, but linguistic nuances and context are lost
Solution Approach 1:
The patent implements a feedback mechanism where the AI system continuously monitors conversation output for biased language patterns related to protected attributes. When bias is detected, the system provides immediate feedback by replacing the biased language with neutral alternatives, thereby maintaining conversation efficiency while preserving linguistic nuances and context that would otherwise be lost.
3Reliability
If AI models are retrained to recognize and mitigate discrimination, then fairness in conversations is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the AI model with comprehensive datasets that include diverse linguistic patterns and contextual information related to protected attributes. This preliminary training equips the system with the ability to automatically recognize and mitigate discrimination without requiring complex real-time intervention mechanisms, thereby improving fairness while managing system complexity.
Data Source
AI summary
Methods, systems, and computer program products for automatically modifying responses from generative models using artificial intelligence techniques are provided herein. A computer-implemented method includes obtaining data pertaining to at least one conversation involving at least one automated conversation exchange software program and at least one user; identifying, among words proposed by the at least one automated conversation exchange software program in connection with the at least one conversation, words qualifying as belonging to one or more predetermined categories by processing the obtained data using artificial intelligence techniques; determining, by processing the identified words and at least one word-based data source, one or more alternate words; modifying at least a portion of the proposed words by replacing at least a portion of the identified words with at least a portion of the one or more alternate words; and performing at least one automated action based on the modifying.