Moral Decision Network Using Group and Individual Neural Networks
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Solution Overview
Problem
Current AI systems lack the capability to make objective moral decisions, as moral dilemmas are subjective and nebulous, and there is a need for AI to develop ethical decision-making capabilities, especially in scenarios like autonomous vehicles where quantitative guidance is absent.
Innovation Solution
A decision network comprising a trained group artificial neural network (ANN) and an individual ANN, along with a fusion block, is used to produce a decision output based on scenario parameters, allowing the system to learn and represent abstract moral values without assuming underlying probability distributions, thereby predicting individual moral decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI systems are equipped with ethical decision-making capabilities, then moral decision-making ability is improved, but subjectivity and lack of quantitative guidance worsen the reliability of decisions
Solution Approach 1:
The patent introduces an intermediary representation layer that translates subjective moral preferences into quantitative vectors. The neural network learns to map scenario parameters and preference data into a shared vector space where moral decisions can be made objectively through mathematical operations on these vectors, bridging the gap between subjective ethics and objective computation.
Solution Approach 2:
The patent transforms moral decision-making parameters by representing moral preferences as learnable vector embeddings rather than fixed rules. The system changes the parameter representation from qualitative ethical principles to quantitative vector forms that can be processed mathematically, enabling reliable automated decision-making while preserving moral adaptability.
2Measurement precision
If group training data is used to train the neural network, then cultural value representation is improved, but individual decision uniqueness deteriorates
Solution Approach 1:
The patent segments the training data into two distinct components: group-level training data that captures cultural values and individual-level training data that captures personal preferences. The neural network is trained separately on these two data types, with the group training establishing a baseline cultural framework and individual training fine-tuning for personal decision-making patterns, thus preserving both cultural representation and individual uniqueness.
Solution Approach 2:
The patent applies local quality by allowing different parts of the decision-making system to have different characteristics. The group-trained components provide stable cultural value representation, while the individually-trained components provide adaptive personal decision-making. This localized differentiation enables the system to simultaneously achieve precise cultural representation and versatile individual decision capability.
3Ease of manufacture
If traditional probability distribution assumptions are made, then mathematical tractability is improved, but applicability to moral dilemmas worsens due to lack of underlying distributions
Solution Approach 1:
The patent substitutes traditional probabilistic mechanics with a deterministic neural network-based approach. Instead of relying on probability distributions and statistical assumptions, the system uses learnable vector representations and neural network computations to model moral preferences. This replacement maintains mathematical tractability through differentiable operations while greatly expanding applicability to moral dilemmas where no underlying probability distributions exist.
Data Source
AI summary
A decision network for moral decision-making includes a trained group artificial neural network (ANN), a trained individual ANN, and a fusion block. The trained group ANN is configured to receive a selected input vector and to produce an estimated en group output based, at least in part, on the selected input vector. The trained group ANN is trained based, at least in part, on group training data including a plurality of group input vectors and corresponding training group outputs. Each group input vector includes a plurality of scenario parameters. The trained individual ANN is configured to receive the selected input vector and to produce an estimated individual output based, at least in part, on the selected input vector. The trained individual ANN is trained after the trained group ANN is trained.