Learning Model Lineage Tracking for Ethical Platform Publication
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Solution Overview
Problem
Existing AI learning models published on public platforms may not comply with revised AI ethics codes, and derived models from these initial models may also fail to meet ethical standards, leading to potential violations and unauthorized use of copyrighted or ethically problematic content.
Innovation Solution
An information processing apparatus that manages learning models by tracking their history and base models, using traceability information to determine and control the publication range based on ethical and legal criteria, ensuring that derived models are appropriately labeled as private or limited in their accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If learning models are published on a public platform without tracking their creation history and base models, then model publication is simple and fast, but it becomes impossible to ensure compliance with revised AI ethics codes and control the publication range of derived models
Solution Approach 1:
The system performs preliminary actions by recording learning history information and base model information at the time of model creation. This preliminary tracking enables future verification of AI ethics compliance without requiring complex real-time monitoring systems, resolving the contradiction between reliability and complexity.
Solution Approach 2:
The determination unit provides feedback by checking recorded learning history and base model information against revised AI ethics codes. This feedback mechanism enables the system to maintain compliance reliability through periodic verification rather than requiring overly complex continuous monitoring systems.
2Reliability
If the system tracks learning history and base models to ensure AI ethics compliance, then compliance with revised codes is maintained, but the complexity of managing publication ranges increases
Solution Approach 1:
The system performs self-service by automatically determining publication ranges based on pre-recorded learning history and base model information. The determination unit autonomously checks compliance and sets publication ranges without requiring manual intervention, maintaining ease of operation while ensuring reliability.
Solution Approach 2:
By preliminarily recording all necessary information at model creation time, the system eliminates the need for complex ongoing management operations. The preliminary data collection enables automated compliance checking, maintaining ease of operation while ensuring AI ethics compliance.
3Adaptability or versatility
If derived models are allowed to be published without restriction, then model utilization and innovation are promoted, but unethical or unauthorized content may be distributed through incremental learning
Solution Approach 1:
The determination unit provides feedback by checking derived models against recorded base model information and learning history. This feedback mechanism identifies models that may propagate unethical content while still allowing legitimate derived models to be published, balancing adaptability with harm prevention.
Solution Approach 2:
The determination unit acts as an intermediary between model creators and the public platform. It mediates by evaluating derived models against ethical standards before allowing publication, enabling model utilization flexibility while preventing harmful content distribution through incremental learning.
4Reliability
If publication range is strictly controlled for all models, then AI ethics compliance is ensured, but legitimate model sharing and derivation are restricted
Solution Approach 1:
The system applies local quality by differentiating publication ranges for different models based on their specific learning history and base model characteristics. Not all models receive the same publication restrictions; the determination unit tailors publication ranges to individual model cases, ensuring compliance while maintaining publication efficiency for legitimate models.
Solution Approach 2:
The system changes parameters by adjusting publication ranges based on specific model characteristics and compliance status. The determination unit modifies publication parameters (public, limited, private) according to the learned model's history and ethical compliance, balancing reliability with productivity by allowing efficient publication where appropriate.
Data Source
AI summary
An information processing apparatus for managing a plurality of learning models published on a model public platform, comprises: a management unit that manages, for each of the plurality of learning models, first information concerning a learning history of the learning model, and second information concerning a base model used to create the learning model; and a determination unit that determines a publication range of each of the plurality of learning models on the model public platform based on both the first information and the second information.