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

Carl,
following my earlier email response, let me make explicit (...)
a fundamental point that perhaps came across as implied (...)

misrepresentation  miscategorization  correctness
transparency, accountability,reliability verifiability
and all sorts of AI flaws and errors  = AI challenges
 can be addressed at least in part with KR
and mitigated through explainability
however
and that the  field of XAI, based on a review of the state of the art,
has become paradoxically inextricable  and unexplainable in its own right

Proposed approaches must tackle directly the challenges, and possibly be
supported with some evidence/proof of their effectiveness
(usefulness notwithstanding)

P

s, or making AI more transparent, more reliable, more accountable
that

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