Context-Based AI Firewall for Learning Entities
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Solution Overview
Problem
Current AI systems lack effective mechanisms to prevent the learning of inappropriate or offensive information, which can lead to undesirable behavior towards users, as they may ingest and mimic negative sentiments or behaviors from their training data.
Innovation Solution
A computer-implemented method using a firewall to filter input information for AI entities based on predefined policies and thresholds, evaluating characteristics such as tone, sentiment, and author profiles to determine if the information is inappropriate, and selectively blocking or modifying the information to prevent undesirable learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI entities learn from diverse input information to improve their intelligence and adaptability, then their learning capability and versatility are enhanced, but they may ingest inappropriate or offensive content that leads to undesirable behavior
Solution Approach 1:
A firewall entity is introduced as an intermediary between the input information source and the AI learning system. The firewall evaluates characteristics of incoming information against predefined policies and thresholds, selectively blocking inappropriate content while allowing beneficial information to pass through, thus protecting the AI system from harmful inputs without compromising its learning capability
Solution Approach 2:
The system performs preliminary evaluation of input information characteristics before the AI entity processes or learns from the data. By assessing tone, sentiment, author profiles, and other characteristics in advance and comparing them against policies, the system prevents inappropriate content from reaching the learning AI, thereby avoiding undesirable behavior before it can occur
2Object-affected harmful factors
If a firewall is introduced to filter inappropriate information from AI learning inputs, then harmful content is blocked, but the system complexity increases
Solution Approach 1:
The filtering system is segmented into distinct functional modules: characteristic evaluation components that analyze specific aspects of input information (tone, sentiment, author profiles), policy storage modules that define acceptable content criteria, and decision-making logic that compares evaluations against policies. This modular segmentation manages complexity by organizing the firewall functionality into manageable, independent components
3Measurement precision
If comprehensive evaluation of information characteristics is performed to accurately identify inappropriate content, then filtering accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The evaluation process focuses on specific local characteristics of input information that are most relevant to determining appropriateness, such as tone, sentiment, and author profiles. Rather than analyzing every aspect of the information equally, the system concentrates computational resources on evaluating these key local qualities against relevant policy criteria, achieving accurate filtering without excessive processing overhead
Data Source
AI summary
Detecting and blocking content that can develop undesired behavior by artificial intelligence (AI) entities toward users during a learning process is provided. Input information is received for a set of one or more AI entities. Characteristics of the input information are evaluated based on rules of a selected policy from a set of policies and learned characteristics of information associated with a corpus of information. It is determined whether a result of evaluating the characteristics of the input information exceeds a predefined threshold. In response to determining that the result of evaluating the characteristics of the input information exceeds the predefined threshold, the input information for the set of AI entities is filtered by performing a selective filtering action, using a firewall, based on context of the input information.