Human Trust Overlays for Automated Data Confidence
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Solution Overview
Problem
Automated algorithms often make decisions with significant ethical, moral, or business implications without adequate mechanisms to assess their trustworthiness, leading to potential harm, including financial, reputational, and even life-threatening consequences, as they lack the ability to distinguish between trustworthy and untrustworthy data.
Innovation Solution
Implementing a data confidence fabric (DCF) system that incorporates user overlays to provide trusted user input, allowing for the assignment of confidence scores to data based on various trust insertion technologies, enabling applications to make informed decisions in high-risk situations by combining machine and human confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated algorithms make decisions without human intervention, then productivity is improved, but reliability deteriorates due to lack of trust assessment in high-risk decisions
Solution Approach 1:
The patent introduces a human-in-the-loop intermediary mechanism where human users act as mediators between automated algorithms and high-risk decisions. The system selectively engages human judgment for decisions flagged as high-risk, allowing automated processing to continue for routine decisions while maintaining reliability for critical outcomes.
Solution Approach 2:
The system dynamically adjusts the level of automation based on risk assessment. Decisions are categorized by risk level, with automated algorithms operating independently for low-risk decisions and human intervention required for high-risk decisions. This dynamic approach optimizes productivity while ensuring reliability where needed.
2Reliability
If human authorization is required for high-risk decisions, then reliability is improved, but productivity deteriorates due to decision delays
Solution Approach 1:
The patent applies different quality standards to different decisions based on their risk characteristics. High-risk decisions receive enhanced human review and accountability measures, while low-risk decisions proceed through streamlined automated processes. This local differentiation ensures reliability for critical decisions without sacrificing overall productivity.
3Productivity
If automated algorithms process all decisions, then productivity is improved, but harmful factors increase due to inability to recognize ethical or moral implications
Solution Approach 1:
The system implements feedback loops where human users provide guidance and correction signals to the automated algorithm. When humans intervene in high-risk decisions, their judgments feed back into the system to refine future automated decision-making, helping the algorithm learn to recognize ethical and moral implications over time.
Data Source
AI summary
A social overlay is provided that allows an application to seek human input when performing high-risk decisions. The overlay allows an application to obtain data confidence scores when performing operations and to obtain human confidence scores that can be used to seek input from users with high confidence scores. This allows applications to improve performance and avoid situations where an automated application may make a wrong decision that does not account for the associated risk.