AI Decision Reconciliation via Causal Empathy Modeling
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Solution Overview
Problem
Conventional systems for automated assessment of requests by AI models lack empathy and fairness, often incorporating biases based on protected attributes like age, gender, race, and caste, and fail to consider the needs and outcomes related to the person and institutions, leading to unfair and biased decisions.
Innovation Solution
A method that constructs a causal model from extracted attributes of a person, determining a utility function for AI models based on empathy criteria for both the person and institutions, and calculating optimin-point information to reconcile decisions and render a fair and empathetic decision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional AI models are used for automated assessment, then decision-making efficiency is improved, but fairness and empathy in decisions deteriorate due to biases based on protected attributes
Solution Approach 1:
The system extracts and removes protected attributes (gender, age, race, caste) from the decision-making process by using causal models to identify and eliminate biased pathways. The causal model separates legitimate decision factors from discriminatory ones, extracting only the fair and empathetic components for final decisions.
Solution Approach 2:
The causal model acts as an intermediary between the AI model's raw decisions and the final output. It mediates by analyzing causal relationships, identifying biases through counterfactual reasoning, and adjusting decisions to ensure fairness and empathy while maintaining decision-making efficiency.
2Measurement precision
If multiple AI models are used for assessment, then decision accuracy is improved, but system complexity increases due to need for reconciliation
Solution Approach 1:
The system merges multiple AI models' decisions into a unified fair and empathetic decision using a single causal model framework. Instead of maintaining separate reconciliation systems for each model combination, the causal model integrates and harmonizes all decisions through a unified approach, reducing overall system complexity.
Solution Approach 2:
The causal model serves multiple functions simultaneously: it analyzes causal relationships, identifies biases, performs counterfactual reasoning, and reconciles decisions from multiple AI models. This multi-functional approach eliminates the need for separate specialized systems, reducing complexity while maintaining high decision accuracy.
3Manufacturing precision
If empathy criteria are incorporated into AI decision-making, then fairness towards persons is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing data through the causal model to identify causal relationships and potential biases before final decision-making. Counterfactual reasoning and bias identification are conducted in advance, reducing the computational burden during actual decision execution while maintaining high fairness standards.
Solution Approach 2:
The system applies empathy criteria selectively based on the specific decision context and identified biases. Rather than uniformly applying all empathy considerations to every decision, the causal model determines which empathy criteria are relevant and necessary, reducing unnecessary computational overhead while ensuring fairness where needed.
Data Source
AI summary
In an embodiment, operations include receiving first information associated with a first person and a first request of the first person to one or more institutions. A set of attributes of the first person is extracted and used to construct a causal model. The causal model represents causal relationships amongst attributes of the set of attributes. For the first person, a utility function associated with each of a plurality of AI models associated with the one or more institutions is determined. The utility function is determined based on the causal model, a first set of empathy criteria associated with the first person, and a second set of empathy criteria associated with each of the one or more institutions. For the utility function, optimin-point information is determined to reconcile a plurality of decisions taken by the plurality of AI models for the first request. The reconciled decision is rendered.