Below is an overview of the current discussion topics within the Trusted AI Committee. Further updates will follow as the committee work develops.
Focus of the committee is on policies, guidelines, tooling and use cases by industry
Survey and contact current open source Trusted AI related projects to join LF AI efforts
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
If you are interested in getting involved please email email@example.com to be added to the mailing list.
AT&T, Amdocs, Ericsson, IBM, Orange, TechM, Tencent
|Animesh Singh||North America||IBMfirstname.lastname@example.org|
|Nat Subramanian||Tech Mahindra|
- 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 reproducibility of generated datasets and AI models
|AI Fairness 360||https://github.com/IBM/AIF360||http://aif360.mybluemix.net/|
|Adversarial Robustness 360||https://github.com/IBM/adversarial-robustness-toolbox||https://art-demo.mybluemix.net/|
|AI Explainability 360||https://github.com/IBM/AIX360||http://aix360.mybluemix.net|
If you are interested in getting involved please email email@example.com for more information.