Re: AI KR Strategist, explainabiilty, state of the art

I agree that Dave's initial email did focus on explainability, but I share Paola's concern about subsequent focus since Carl's email says the purpose of the series of calls  is " to expand on the AI Strategist work that focused on leveraging StratML" - the documents attached seemed to be all about the job description of AI KR Strategist Role and included the text "explain" only as follows, with relation to glossaries:

Goal Statement: Employ definitions from one or more glossaries when explaining AIKR object audit data, veracity facts and (human, social and technology) risk mitigation factors So that (business) people more readily understand the value that the glossaries bring.

To speak for myself, I may be interested (though with little time available) in technical KR techniques and representations that facilitate explainability; especially in bridging the gap between academic research and practical enterprise application.
But I'm not at all interested in the role description side of things (even role objectives to "ensure explaniability"). Or anything strategy-related (organization level as opposed to agent strategy). I'm not saying it's unimportant, just not my interest.

Regards
Pete

Pete Rivett (pete.rivett@federatedknowledge.com)
Federated Knowledge, LLC (LEI 98450013F6D4AFE18E67)
tel: +1-701-566-9534
Schedule a meeting at https://calendly.com/rivettp



________________________________
From: Paola Di Maio <paoladimaio10@gmail.com>
Sent: Friday, November 8, 2024 4:53 PM
To: carl mattocks <carlmattocks@gmail.com>
Cc: W3C AIKR CG <public-aikr@w3.org>
Subject: Re: AI KR Strategist, explainabiilty, state of the art

Thank you Carl
Given the vastity and complexity of the subject matter
I am suggesting that
perhaps if you could write a couple of lines of summary of what the issues under discussion are and how the proposed approach addresses the issue, etc etc etc
if you could, at your convenience
 Cheers
P

On Sat, Nov 9, 2024 at 12:42 AM carl mattocks <carlmattocks@gmail.com<mailto:carlmattocks@gmail.com>> wrote:
Paola

Please note in the email chain there are statements about 'explainability' which continues to be an issue . .. thus the focus of the proposed effort.

Carl

On Fri, Nov 8, 2024, 5:41 PM Paola Di Maio <paola.dimaio@gmail.com<mailto:paola.dimaio@gmail.com>> wrote:
Carl, good to hear from you and thanks
for picking up where you left .

Btw the attachment you sent never made it into W3C Group reports, maybe at some point you d like to publish them with some notes explaining how these addressed the challenges discussed? The documents you send do not seem to explain how the proposed work fits in the AI KR mission (which problem they solve).

As previously discussed StratML can be a useful mechanism represent knowledge, at syntactic level. A markup language by itself it does not address nor resolve the key challenges faced by AI today that KR (thinking semantics here) as a whole could tackle. (irrespective of any implementation language of choice).

In the work you propose, there is strong coupling between AI KR and StratML as a syntax
(your construct binds the two) This approach may be suitable in a Stratml CG (is the one by the way)? rather than an AI KR CG  The focus is AI KR, rather than a modeling language by itself

If the line you are interested to explore is StratML only, it could be useful if you (or other proponents of this line of work) could  summarise   how it address the broader AI KR challenges.
For example, say, knowledge misrepresentation - or miscategorization - or wrong recommendations, or making AI more transparent, more reliable, more accountable etc.

Perhaps  show how these can be addressed  with use cases or other proof of concept.

So basically, I encourage discussions to be focused on AI KR  and whatever line of work members propose, please make it clear which problem each construct intends to resolve in relation to the overall mission.

Thank  you!

Paola Di Maio, PhD


On Thu, Nov 7, 2024 at 6:09 PM carl mattocks <carlmattocks@gmail.com<mailto:carlmattocks@gmail.com>> wrote:
Greetings All - It has been a while.

Given the interest in AI , I am proposing that we set up a series of online meetings to expand on the AI Strategist work that focused on leveraging StratML. (see attached).

The topics include:

  1.  AI Observability Mechanisms (monitor behavior, data, and performance)
  2.  KR Models used in the explanations (to a given audience, and what concepts are needed for this)
  3.  KR ID needed for Knowledge Content (UID, URI) Logistics management
  4.  Roles of Humans in the Loop (as a creator, and an audience type)
  5.  Agents having Authority awarded by a Human in the Loop
  6.  Catalogs of AI capabilities ( see Data Catalog (DCAT) Vocabulary<https://www.w3.org/TR/vocab-dcat-3/> )
  7.  AIKR Using / Used in DPROD (specification provides unambiguous and sharable semantics) https://ekgf.github.io/dprod/


Timeslots for meetings  will be determined by participants.  Please let me know if you are interested.

Thanks

Carl Mattocks

CarlMattocks@WellnessIntelligence.Institute
It was a pleasure to clarify


On Tue, Jun 11, 2024 at 5:24 AM Dave Raggett <dsr@w3.org<mailto:dsr@w3.org>> wrote:
First my thanks to Paola for this CG. I’m hoping we can attract more people with direct experience. Getting the CG noticed more widely is quite a challenge! Any suggestions?

It has been proposed that without knowledge representation. there cannot be AI explainability

That sounds somewhat circular as it presumes a shared understanding of what “AI explainability” is.  Humans can explain themselves in ways that are satisfactory to other humans.  We’re now seeing a similar effort to enable LLMs to explain themselves, despite having inscrutable internal representations as is also true for the human brain.

I would therefore suggest that for explainability, knowledge representation is more about the models used in the explanations rather than in the internals of an AI system. Given that, we can discuss what kinds of explanations are effective to a given audience, and what concepts are needed for this.

Explanations further relate to how to making an effective argument that convinces people to change their minds.  This also relates to the history of work on rhetoric, as well as to advertising and marketing!

Best regards,

Dave Raggett <dsr@w3.org<mailto:dsr@w3.org>>