Bias Identification Engine for Cognitive Computing Systems
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Solution Overview
Problem
Cognitive computing systems often introduce bias during training, making it difficult to identify and correct, which can lead to unfair or inaccurate outcomes in decision-making processes, such as gender bias in training datasets.
Innovation Solution
A bias identification engine is implemented to detect bias in cognitive computing systems by using a bias risk annotator that analyzes inputs and outputs for bias triggers, and a bias source identification engine that distinguishes between bias from training data and data corpora, providing notifications to administrators for corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cognitive computing systems are trained using large datasets, then the system's processing capability and intelligence are improved, but bias is introduced into the system leading to unfair or inaccurate outcomes
Solution Approach 1:
The patent applies preliminary action by implementing a bias identification engine that proactively detects bias triggers in training datasets before they can negatively impact the cognitive computing system's outcomes. The system pre-identifies and flags potential bias sources, allowing corrective actions to be taken during the training process rather than discovering bias issues after deployment.
Solution Approach 2:
The patent introduces an intermediary component - the bias identification engine - that acts as a mediator between the training dataset and the cognitive computing system. This intermediary analyzes inputs and outputs for bias triggers, providing notifications to administrators about potential bias sources, thereby preventing biased data from directly influencing the system's decision-making processes.
2Reliability
If bias identification mechanisms are added to cognitive computing systems, then fairness and accuracy are improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the bias identification function into distinct modular components: a bias identification engine with a bias risk annotator, and a bias source identification engine. This segmentation allows each component to have a specific, focused function - identifying bias triggers in data versus identifying bias sources in system operations - making the overall system more manageable and maintainable despite the added complexity.
Solution Approach 2:
The bias identification engine serves as an intermediary layer that interfaces with the existing cognitive computing system without fundamentally restructuring it. The engine provides bias information through notifications to administrators, allowing the core system to continue operating while the intermediary handles the complexity of bias detection and reporting separately.
3Measurement precision
If bias triggers are monitored in real-time, then bias detection capability is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring the bias risk annotator with a dictionary of known bias triggers and patterns. This preliminary preparation allows the system to quickly match incoming data against established bias criteria without performing complex analysis in real-time, thereby maintaining high detection capability while minimizing processing time overhead.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the sensitivity and scope of bias trigger monitoring based on system needs. The bias identification engine can modify its detection parameters - such as the strictness of trigger matching or the types of bias triggers monitored - to balance detection precision with processing efficiency depending on the specific application context.
Data Source
AI summary
Mechanisms are provided to implement a bias identification engine that identifies bias in the operation of a trained cognitive computing system. A bias risk annotator is configured to identify a plurality of bias triggers in inputs and outputs of the trained cognitive computing system based on a bias risk trigger data structure that specifies terms or phrases that are associated with a bias. An annotated input and an annotated output of the trained cognitive computing system is received and processed by the bias risk annotator to determine if they comprise a portion of content that contains a bias trigger. In response to at least one of the annotated input or annotated output comprising a portion of content containing a bias trigger a notification is transmitted, to an administrator computing device, that specifies the presence of bias in the operation of the trained cognitive computing system.