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>
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> 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>
>> 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> 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>
>>>>
>>>>
>>>>
>>>>