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

Paola

To be explicit ... I am not proposing to focus exclusively on Explainable
Artificial Intelligence
<https://www.darpa.mil/program/explainable-artificial-intelligence> (a
suite of machine learning techniques that: Produce more explainable models )
I do expect to have discussions about models used in the explanations about
KR used in AI.

cheers

Carl


It was a pleasure to clarify


On Sat, Nov 9, 2024 at 2:10 AM Paola Di Maio <paoladimaio10@gmail.com>
wrote:

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