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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

Mailing List

If you are interested in getting involved please email info@lfai.foundation to be added to the mailing list. 

Current Participants

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

Chairs

NameRegionOrganizationContact Info
Animesh SinghNorth AmericaIBMsinghan@us.ibm.com
Souad OualiEuropeOrangesouad.ouali@orange.com
Jeff CaoAsiaTencentjeffcao@tencent.com


Working Group:

Name

Organization

 Contact Info
Ofer HermoniAmdocs ofer.hermoni@amdocs.com 
 Mazin GilbertATT mazin@research.att.com 
 Alka RoyATT AR6705@att.com 
Mikael Anneroth Ericssonmikael.anneroth@ericsson.com 

Jim Spohrer

IBM

 spohrer@us.ibm.com

Maureen McElaney

IBM

 mmcelaney@us.ibm.com

Susan Malaika

IBM

 malaika@us.ibm.com
Francois Jezequel Orangefrancois.jezequel@orange.com 
Nat SubramanianTech Mahindra 

Natarajan.Subramanian@Techmahindra.com

 Han Xiao Tencent hanhxiao@tencent.com

Meetings

How to Join:

Contact  info@lfai.foundation for more information about how to join.

Meeting Content (minutes / recording / slides / other):


DateMinutes

 

  • Goals defined for the meeting:
  • Assign chairs to two working groups

    1. AI Principles Working Group
    2. AI Use Cases Working Group
    3. Possible Discussion about third working group

  • Discussion about LFAI day in Paris

  • More next steps
    • Will begin recording meetings in future calls.

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:


If you are interested in getting involved please email info@lfai.foundation for more information. 

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