Overview

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

Current Participants

• AT&T, Amdocs, Ericsson, IBM, Orange, TechM, Tencent
  
  

Chairs

• Animesh Singh : North America (IBM)

• Souad Ouali : Europe (Orange)

• Jeff Cao : Asia (Tencent)

Working Group:

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 reproducibility of generated datasets and AI models

Projects:

Name	Github	Website
AI Fairness 360	https://github.com/IBM/AIF360	http://aix360.mybluemix.net
AI 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 info@lfai.foundation for more information.