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Overview
Below is an overview of the current discussion topics within the LF AI Ethics Committee. Further updates will follow as the committee work develops.
Create a badging or certification process for open source projects that meet the Trusted AI policies/guidelines defined by LF AI
Create a document that describes the basic concepts and definitions in relation to Trusted AI and also aims to standardize the vocabulary/terminology
Current Participants
AT&T, Amdocs, Ericsson, IBM, Orange, TechM, Tencent
Chairs
Sub Categories:
- Fairness: Methods to detect and mitigate bias in datasets and models, including bias against known protected populations
- Robustness: Methods to detect alterations/tampering with datasets and models, including alterations from known adversarial attacks
- Explainability: Methods to enhance understandability/interpretability by persona/roles in process of AI model outcomes/decision recommendations, including ranking and debating results/decision options
- Lineage: Methods to ensure provenance of datasets and AI models, including reproducability of generated datasets and AI models
Working Group:
Name | Organization | Email |
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Jim Spohrer | IBM |
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Maureen McElaney | IBM |
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Susan Malaika | IBM |
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