AI KR (Artificial Intelligence Knowledge Representation) Community Group Wiki
These pages are used by members of the AI KR (Artificial Intelligence Knowledge Representation) Community Group for collaborative work.
In brief: - AI KR is a very broad topic. and can be tackled from different perspectives with a variety of approaches. As there is renewed interested in AI. a number of gaps were identified especially in relation to ML, Machine Learning that need addressing, in practice, in education and in scholarly research. This group identifies a number of these issues and addresses them with discussions and some community and scholarly activities.
A study conducted by PDM in AI KR Education is in the works, with the aim to identify and address knowledge gaps in AI KR (contributors needed from diverse countries and languages)
Simply put, Knowledge representation is defined by the method used to explain or decode knowledge
Meta : explains the types of Knowledge and logical reasoning used e.g. Spatial-Temporal reasoning. Heuristic : explains the knowledge of a subject expert e.g. Frame facet. Procedural : explains how to achieve a desired outcome e.g. Event-condition-action rule. Declarative : explains facts, concepts, principles and relationship to planned behavior e.g. Mathematical equation. Structural : explains types of relationship that exist between concepts/ objects e.g. Semantic Network.
The links below provide information about specific methods
- Zotero KR Library Please enter new library resources and help to sort the folder structure
Report Draft AI KR Guideline: KR for eGovernance and Trustworthy AI
The intent of the guideline for eGovernance of Knowledge Representation for a Trustworthy AI (Artificial Intelligence) system is to promote good governance. As in, the implementation of AIKR (Artificial Intelligence Knowledge Representation) eGovernance processes and functions will enable accountable and transparent governance. It is based on:
Artificial Intelligence describes a system that mimics "cognitive" functions which humans associate with the human mind, such as "learning" and "problem solving". AIKR can be (1) meta-representation information about the AI system’s own state, and/or (2) information about a domain, in a form that enables an AI system to provide a solution for a specific problematic task.
A Trustworthy AI System is said to have three characteristics that are part of its entire life cycle: 1. Lawful: AI System complies with all applicable laws and regulations, such as, provision audit data defined by a governance operating model; 2. Ethical: AI System ensures adherence to pivotal principles, such as, confidentiality, autonomy, accountability and veracity; 3. Robust: AI System is designed to handle uncertainty and tolerate perturbation from a likely threat perspective, such as, design considerations incorporate human, social and technology risk factors
- Shared KR for AI Commons - The heart of AI Commons is Shared KR
Paper Draft status: needs advancing
- Deep KR for EGovernance, Talk, Taipei September 2019/
- KR Meets Brain Science (Conference forthcoming)
Paper Draft, Not Shared - HAVE SLIDES! WORKING ON THE PAPER!
FEEDBACK TO OTHER ORGANISATIONS
Online Meetings dates and agendas
Please link here the dates and agendas and minutes for the meetings? So that participants can also add to the agendas and edit the minutes
11 February 2020 []
Leveraging the StratML specification for AIKR using KAIROS Stakeholder(s): AIKR CG https://www.w3.org/community/aikr/welcome
Role: AI KR Srategist Vision AIKR Strategist uses StratML vocabulary and a KAIROS approach to monitor performance of AIKR objects implemented by machine learning powered services. Mission To determine if use of performance tracking of AIKR objects implemented by machine learning can provide the foundation for a Trustworthy AI System. Ontological Statement: life cycle characteristics of Trustworthy AI System 1. Lawful: AI System complies with all applicable laws and regulations, such as, provision audit data defined by a governance operating model; 2. Ethical: AI System ensures adherence to pivotal principles, such as, confidentiality, autonomy, accountability and veracity; 3. Robust: AI System is designed to handle uncertainty and tolerate perturbation from a likely threat perspective, such as, design considerations incorporate human, social and technology risk factors.
Goal Machine Learning Evaluation Evaluate machine learning models Stakeholder(s): https://www.w3.org/community/aikr Role: Community of Interest Objective(s): Towards building a foundation for a Trustworthy AIKR - primary objectives are (a) document the vision, values, goals, objectives for one or more AIKR objects, (b) Track AIKR object performance outcome via KPI (Key Performance Indicator) based on supervised learning models measurements; https://www.stratnavapp.com/StratML/Part2/861566c8-e9be-4642-b52f-f673fa499f4e/Styled secondary objective is to employ ontological statements when explaining AIKR object audit data, veracity facts and (human, social and technology) risk mitigation factors..
AIKR Block Chain A talk by PDM Triggered acknowledgement that CG should identify what role blockchain may contribute to Trustworthy AIKR. Commencing with Understanding Blockchain’s Role and Risks in Trusted Systems http://3dpdfconsortium.org/wp-content/uploads/2020/01/ECM_Standards_Blockchain_WhitePaper-Final.pdf
The WG would benefit from increased co-leadership
All members can lead and shape the work being done by providing input in the stakeholder survey (linked to in the group's home page).