Ethical Confidence Fabric for Algorithm Lifecycle Risk Scoring
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Solution Overview
Problem
Current algorithm development processes lack adequate ethical considerations, leading to the creation of algorithms that may cause harm to society or businesses due to unawareness of ethical implications during data discovery, engineering, and deployment.
Innovation Solution
Implementing an Ethical Confidence Fabric (ECF) to annotate and score ethical risks at various stages of the algorithm development lifecycle, generating an Ethics Confidence Score (ECS) to guide ethical compliance and modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional algorithm development processes are used, then development speed and productivity are maintained, but ethical risks and harmful factors increase due to lack of ethical training and considerations
Solution Approach 1:
The patent applies preliminary action by implementing ethical assessments and annotations during the hypothesis formulation and data discovery phases, before the algorithm development proceeds. This allows ethical risks to be identified and addressed early in the lifecycle, preventing harmful outcomes rather than detecting them after development is complete.
Solution Approach 2:
The patent implements feedback mechanisms through ethical confidence scores that are calculated and provided back to developers at various stages of the algorithm lifecycle. These scores feedback on the ethical quality of hypotheses, data, and algorithms, enabling continuous improvement of ethical reliability throughout the development process.
2Reliability
If ethical assessments and annotations are added at each stage of algorithm development, then ethical confidence score improves, but development time and process complexity increase
Solution Approach 1:
The patent segments the ethical assessment process into distinct phases corresponding to the algorithm lifecycle stages: hypothesis formulation, data discovery, data engineering, and algorithm deployment. Each phase has specific annotation requirements and assessment criteria, allowing ethical evaluation to be distributed across multiple smaller tasks rather than concentrated in a single time-consuming review process.
Solution Approach 2:
The system enables self-service ethical assessment through automated annotation of hypotheses and data with ethical metadata, and automated calculation of ethical confidence scores. This reduces the time burden on developers by providing self-evaluating mechanisms that do not require extensive manual review while still maintaining high ethical standards.
3Measurement precision
If comprehensive ethical metadata annotation is implemented, then measurement precision of ethical risks improves, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent implements a universal ethical metadata schema that can annotate multiple types of objects (hypotheses, data, algorithms) with consistent ethical risk categories and confidence scores. This multi-functional annotation system handles diverse ethical assessment requirements across different algorithm lifecycle stages using a unified framework, reducing system complexity compared to having separate assessment mechanisms for each object type.
Solution Approach 2:
The system manages complexity by changing parameters of the ethical assessment - using standardized ethical risk categories, confidence score ranges, and metadata structures that can be adjusted based on the specific phase and type of algorithm development. This allows precise measurement of ethical risks while maintaining manageable system complexity through parameterized assessment rather than fixed complex procedures.
Data Source
AI summary
One example method includes formulating a hypothesis for development of computing model, annotating the hypothesis with ethics metadata, storing the hypothesis and the ethics metadata, in association with each other, in a ledger, performing ‘n’ phases of a development lifecycle for the computing model, annotating each of the ‘n’ phases with ethics metadata specific to the phase, updating the ledger to include the ‘n’ phases and the ethics metadata respectively associated with each of the ‘n’ phases, and calculating an ethics confidence score for the computing model.