Morality Assessment System Using Common-Sense Vector Integration
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Solution Overview
Problem
Current morality estimation methods using natural language processing fail to capture common-sense characteristics and underlying moral concepts, leading to incomplete understanding and 'reward hacking' due to the reliance on purely text-generated features without deeper contextual analysis.
Innovation Solution
A system and method utilizing artificial intelligence that extracts common-sense characteristics from input data by generating vectors through a combination of language and common-sense models, incorporating counterfactual data for training, and assigning weightages to these characteristics to determine morality values, allowing for more accurate and contextually aware morality assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If purely text-generated features are used for morality estimation, then the system is simple to implement, but the accuracy of morality assessment deteriorates due to missing implicit knowledge and common-sense characteristics
Solution Approach 1:
The patent combines multiple models (language model, common-sense model, and morality assessment model) into an integrated system. The language model generates contextual features, the common-sense model extracts implicit knowledge, and these are merged to improve morality assessment accuracy while maintaining implementation feasibility through modular architecture
Solution Approach 2:
The system uses composite feature representations by combining text-generated features from the language model with common-sense characteristics from the common-sense model. This composite approach creates a more robust feature set that captures both explicit text meaning and implicit moral context
2Use of energy by moving object
If contextual information is captured to a degree using text-generated features, then the processing is computationally efficient, but implicit knowledge and deeper understanding are lost
Solution Approach 1:
The common-sense model acts as an intermediary between the text input and morality assessment. It processes the input data to extract implicit common-sense characteristics that bridge the gap between surface-level text and deeper moral understanding, preventing information loss while maintaining computational efficiency
3Measurement precision
If common-sense characteristics are extracted using multiple models, then the morality assessment accuracy improves, but the system complexity increases
Solution Approach 1:
The system segments the morality assessment task into distinct components: a language model for contextual features, a common-sense model for implicit knowledge extraction, and a morality assessment model for final evaluation. This segmentation allows each model to specialize in specific functions, improving overall accuracy while managing complexity through modular design
4Measurement precision
If counterfactual data is used for training, then the accuracy with lesser amount of data is improved, but the data processing complexity increases
Solution Approach 1:
The system performs preliminary processing of training data by generating counterfactual examples that explicitly represent moral distinctions. This preliminary action prepares the data in a form that is more effective for training, improving accuracy with less data while the complexity is managed through automated counterfactual generation processes
Data Source
AI summary
Systems and methods are provided for assessing morality of a user. A request comprising an input data is received over a communication network to assess the morality corresponding to the input data. Upon receiving the request, a first vector is generated through deployment of a predefined language model based on the input data. Then a set of common-sense characteristics are extracted from the input data by generating a corresponding second vector for each of the set of common-sense characteristics from the input data by deploying a common-sense model. Upon generation of the first vector and the second vectors, morality value is determined for the input data based on the first vector and the second vectors corresponding to the set of common-sense characteristics, the morality value indicates whether a context of the input data is morally correct.