Virtual Assistant ML Unlearning for Bias and Privacy Protection
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Solution Overview
Problem
Virtual assistants learn undesirable data over time, leading to issues such as unfair bias, privacy intrusions, and propagation of stale/obsolete information, which existing technologies struggle to address effectively.
Innovation Solution
Deploy AI-based unlearning data identification agents within virtual assistant services and ML models to identify and unlearn data sets meeting predefined criteria, using unlearning algorithms to retrain models based on determined rules, thereby removing biases and outdated information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual assistants continuously learn from user data over time, then their responsiveness and adaptability improve, but privacy intrusions and propagation of stale/obsolete information increase
Solution Approach 1:
The system performs preliminary identification of data sets that meet unlearning criteria before they cause harmful effects. Unlearning agents continuously crawl virtual assistant services and ML models to identify data sets meeting predefined unlearning data criteria, enabling proactive removal of privacy-sensitive information before it can be exploited or cause harm.
Solution Approach 2:
The system selectively discards specific data sets from the ML model while preserving the overall model structure and other useful data. The unlearning algorithms receive identified data sets and determine unlearning rules to retrain the ML model, removing only the harmful or stale portions while maintaining the model's general functionality and adaptability.
2Adaptability or versatility
If virtual assistants continuously learn from user data over time, then their responsiveness and adaptability improve, but propagation of unfair bias increases
Solution Approach 1:
The system implements continuous feedback loops where unlearning agents monitor the virtual assistant services and ML models for data sets exhibiting unfair bias. The identification of data sets meeting unlearning criteria creates a feedback mechanism that detects bias propagation and triggers corrective unlearning actions to maintain fairness while preserving adaptability.
Solution Approach 2:
The system discards data sets that introduce unfair bias while recovering and preserving the beneficial adaptive capabilities of the ML model. The unlearning algorithms selectively remove biased data portions through retraining, maintaining the model's responsiveness to legitimate user needs while eliminating harmful bias patterns.
3Adaptability or versatility
If virtual assistants continuously learn from user data over time, then their responsiveness and adaptability improve, but decisions based on stale/obsolete data increase
Solution Approach 1:
The system performs preliminary identification and removal of stale and obsolete data sets before they can negatively impact decision-making. Unlearning agents continuously monitor and identify data sets meeting unlearning criteria, enabling proactive elimination of outdated information that would otherwise degrade the quality of assistant responses and decisions.
Solution Approach 2:
The system discards stale and obsolete data sets while preserving the model's adaptive capabilities and useful historical information. The unlearning algorithms selectively remove only the outdated portions through targeted retraining, maintaining the model's responsiveness to current user needs while eliminating decisions based on obsolete data.
4Reliability
If unlearning algorithms retrain ML models to remove identified data sets, then privacy protection and bias mitigation improve, but computational resources and training time increase
Solution Approach 1:
The system extracts and removes only the specific harmful or stale data sets from the ML model rather than performing complete retraining. The unlearning algorithms take out identified data sets that meet unlearning criteria and apply targeted removal, significantly reducing computational resources compared to full model retraining while maintaining effective privacy protection.
Solution Approach 2:
The system discards only the necessary harmful portions of the data and recovers/preserves the majority of the model's learned knowledge and capabilities. This selective approach minimizes the retraining burden and computational resource consumption while achieving the required privacy protection and bias mitigation goals.
Data Source
AI summary
Undesirable data used in the Machine Learning (ML) models of virtual assistants is identified and “unlearned” or otherwise forgotten/erased from the models. Agents/monitors are deployed within virtual assistant services and the ML models themselves, such as the NLP models, that intelligently and continuously crawl the services and the ML models to identify data sets that meet predefined criteria (i.e., unlearning data criteria). The unlearning data identification agents/monitors feed identified data sets to unlearning algorithms which determine unlearning rules applicable to the data sets and subsequently are executed on the ML models to retrain the models to unlearn data related to and included within the identified data sets based on the determined unlearning rules.