Re: fatml /stratml templates

Paola, yes, I just created this particular template 
<https://stratml.us/drybridge/index.htm#TSISA> today, using the content 
of FATML.org's principles and impact document 
<https://stratml.us/drybridge/index.htm#SIS4A>.  However, I've created 
other templates previously, such as those at 
https://stratml.us/drybridge/index.htm#FEBPA, which are based upon 
requirements set forth in the U.S. Federal Evidence-Based Policymaking 
Act.  (BTW, those templates are an example of what I mean when I say 
that we have far too much "policy" in narrative format and far too 
actual performance plans in open, standard, machine-readable format.)

The structure and semantics of the elements of StratML Part 2 are common 
to the performance plans and reports of all organizations worldwide (as 
well as less formal groups and individuals who choose to lead 
mission/goal-directed lives and would like to engage others to achieve 
their objectives).   What differs, of course, is the substantive content 
of the actual plans and reports themselves.

In other words, the StratML Part 2 schema itself is a template for ANY 
performance plan or report, but to the degree that the substantive 
content of a plan may be applicable to many organizations, it can be 
used as a model plan for all of them. There's no reason each org should 
be forced (or allowed, if public impacts are involved) to reinvent the 
wheel with respect to content that is applicable to an entire group of 
them (e.g., a regulated industry).

Joe Carmel, who initially developed the StratML XForms, provided the 
magic code that automatically opens the templates in the form.

The form does not write to a server.  The files must be saved locally.  
In one sense, that's good because it means no one needs to maintain UIDs 
and passwords for access to the server.  The downside, of course, is 
that many people will be turned off by having to save the files on their 
PCs and then find places to upload them to the Web.  That's why we need 
the support of folks like Chris Fox and Jorge Sanchez, who are aiming to 
make it as easy as possible for users to create StratML without having 
to worry about any of the details besides the unique content of their 
own plans and reports.  Hopefully, entrepreneurs like them can build 
highly profitable businesses based upon the standard.

As for what can be done with the data, it seems to me the biggest 
impediment to showing people why they should care about the StratML 
standard is the lack of good query/discovery services making salient the 
power of the semantics and structure of the schema.  That's why I'm very 
much looking forward to seeing what Jorge might be able to do with the 
application on which he is working, based on BaseX.

Beyond that, it would be great if we could engage some AI/ML techies to 
demonstrate what they can do with the >4,000 files in the StratML 
collection.  Some of the types of tools, apps, and services that will be 
required are documented at https://stratml.us/carmel/iso/SMLTASwStyle.xml

Please keep the questions, comments, and suggestions coming. Eventually, 
the tools, apps, and services need to be so intuitive to use than no 
training or explanation is required.

Owen

On 4/22/2020 7:24 PM, Paola Di Maio wrote:
> Owen
> Thank you for this template. Is this new?
>  Now let me take a moment to reflect
>
> is this stratml templating facility new, or has it always been there?
> I remember asking some time ago and I dont remember seeing it before 
> but I am
> overloaded and my memory is highly compressed -(to the point of 
> sounding demented at times?)
>
> When the template is filled out , where is it saved? (I think I asked 
> this a couple of years ago or so but dont remember the answer, ah the 
> bliss of dementia)
>
> Is this the usual standard stratml template where the usual stratml 
> elements are been used to map out
> some FAT construct?  I am trying to figure out how much on this form 
> is stratml  and what is fatml
> I cannot distinguish them as such but its very early morning here
>
> This is hot stuff. we should think of a way of making the best use of 
> it and drum it up -
> can you please suggest some ways that it would be beneficial to 
> declare algorithmic fat using ml
> and explain how stratml supports fatml? some quantifiable/verifiable  
> benefits?
>
> (I know the obvious benefits of machine learning, but we could tinker 
> out some specific benefits then we must demonstrate them with some 
> cases and example and document this work as a must do for everyone)
>
> I suppose to evaluate/show/demonstrate/leverage the benefits over a 
> set of examples we need the parser?
>
> we can then write a  short release note from this group, glorify this 
> a bit and maybe do one or two papers
> for workshops
> this is definitely a deliverable and possibly a valuable contribution 
> to the FAT movement
> I am sure
>
> PDM
>
>
>
>
>
>
>
> On Thu, Apr 23, 2020 at 3:21 AM Owen Ambur <Owen.Ambur@verizon.net 
> <mailto:Owen.Ambur@verizon.net>> wrote:
>
>     The template is now available at
>     https://stratml.us/drybridge/index.htm#TSISA with a link that
>     opens it for editing in an XForm.
>
>     Owen
>
>     On 4/21/2020 7:58 PM, Paola Di Maio wrote:
>>     Great stuff Owen
>>     thanks a lot
>>     I am working on integrating FAT  into knowledge representation
>>     The website has a great list of resources to work with,
>>     lets work on this too,
>>     P
>>
>>     On Tue, Apr 21, 2020 at 10:54 PM Owen Ambur
>>     <Owen.Ambur@verizon.net <mailto:Owen.Ambur@verizon.net>> wrote:
>>
>>         FATML.org's about statement is now available in StratML
>>         format at https://stratml.us/drybridge/index.htm#FATML
>>
>>         So too are their Principles for Accountable Algorithms and a
>>         Social Impact Statement for Algorithms.  Like corporate
>>         social responsibility plans and reports, social impact
>>         statements for algorithms should be published on the Web in
>>         an open, standard, machine-readable format like StratML Part 2.
>>
>>         Anyone who is socially responsible enough to do that for
>>         their algorithm could get started as easily as by clicking on
>>         this link
>>         <http://stratml.us/forms/walt5.pl?url=http://stratml.us/carmel/iso/SIS4A.xml>
>>         and editing the document to include the relevant performance
>>         indicators and stakeholder roles.
>>
>>         Might a more generic version of the plan be a good
>>         deliverable for the AIKR CG?
>>
>>         See these StratML use cases:
>>
>>             Goal 4: Corporations
>>             <https://stratml.us/carmel/iso/UC4SwStyle.xml#_1f82f648-083e-11e6-a8aa-42bd45c7ae33>
>>             - Publish corporate social responsibility (CSR) plans and
>>             reports on the Web in open, standard, machine-readable
>>             format.
>>
>>             Goal 30: Artificial Intelligence
>>             <https://stratml.us/carmel/iso/UC4SwStyle.xml#_6f069874-bb92-11e7-9b76-f79f9342c8d9>
>>             - Document on the Web in StratML format the performance
>>             plans of proposed artificial intelligence agents.
>>
>>             Goal 33: Artificial Ignorance
>>             <https://stratml.us/carmel/iso/UC4SwStyle.xml#_7f412cd0-81a7-11ea-8156-25622d83ea00>
>>             - Help human beings overcome their personal biases that
>>             prevent them from attending to evidence that is
>>             applicable to the realization of their objectives.
>>
>>         Owen
>>
>>         On 4/20/2020 10:26 PM, Paola Di Maio wrote:
>>>         Hello Frank
>>>         Thanks for reply and for your interest
>>>         (At the back of my mind I wonder if you are related to Nicola)
>>>
>>>         I am working on FAT AI - yes, there is strong AI. weak AI
>>>         and FAT AI - ha ha
>>>         In particular, I developing a knowledge object for FAT KR,
>>>         fair, accountable transparent
>>>
>>>         https://docs.google.com/drawings/d/1ARnEiubC7bDkSsJzAKvapYISYGANz5D9oOTEvuxR-lE/edit?usp=sharing
>>>         Please note this is an infographic, not a UML nor flowchart
>>>
>>>         I am preparing a lecture and writing up note do nto ahve a
>>>         narrative yet but in sum, we need a way of instilling the
>>>         notion of adequacy
>>>         into KR. At the moment it is a bit notionally done. And FAT
>>>         is one set of such possible evaluation criteria for adequacy
>>>
>>>         (Also others of course)
>>>         I am interested in feedback  on the diagram , can you make
>>>         sense of it?
>>>         can it be clarified/improved?
>>>
>>>
>>>                  I’ve personally spent years working with
>>>                 data-driven schema-less models that help eliminate
>>>                 such biases and open up a world of model
>>>                 representations that allow knowledge to form freely
>>>                 and adjust dynamically to data changes.
>>>
>>>         Please do share your stuff , i d like to include/reference
>>>         it in this work
>>>         cheeers
>>>
>>>         PDM
>>>
>>>         On Tue, Apr 21, 2020 at 9:08 AM Frank Guerino
>>>         <frank.guerino@if4it.com <mailto:frank.guerino@if4it.com>>
>>>         wrote:
>>>
>>>             Hi Paola,
>>>
>>>             This is very interesting.  Thank you for sharing it.
>>>
>>>             In addition to researching bias as a pathology resulting
>>>             from poor knowledge modeling, you may want to also
>>>             consider the reverse (i.e. poor modelling/models that
>>>             result from biases).  One such bias arises from the
>>>             notion that model structures must be pre-designed and
>>>             imprinted in database schemas in order to capture model
>>>             data, forcing data to be restructured/transformed to fit
>>>             the model’s design rather than having the model result
>>>             from the ever changing data, itself.  We see this with
>>>             enterprise modeling tools (e.g. Architecture Modeling
>>>             Tools, Cause & Effect Models, CMDBs, etc.). I’ve
>>>             personally spent years working with data-driven
>>>             schema-less models that help eliminate such biases and
>>>             open up a world of model representations that allow
>>>             knowledge to form freely and adjust dynamically to data
>>>             changes.
>>>
>>>             Another example is “standards” (which are like belly
>>>             buttons because everyone has one). Often, standards
>>>             establish pre-conceived notions and cause severe
>>>             narrowmindedness, yielding the opposite of their
>>>             original intent.
>>>
>>>             There are many such biases that cause bad
>>>             modelling/models and you may want to explore them as well.
>>>
>>>             My Best,
>>>
>>>
>>>             Frank
>>>
>>>             --
>>>
>>>             /Frank Guerino, Principal Managing Partner/
>>>
>>>             */The International Foundation for Information
>>>             Technology (IF4IT)
>>>             /*/http://www.if4it.com
>>>             1.908.294.5191 (M)/
>>>
>>>             /Guerino1_Skype (S)/
>>>
>>>             *From: *Ontolog Forum <ontolog-forum@googlegroups.com
>>>             <mailto:ontolog-forum@googlegroups.com>> on behalf of
>>>             Paola Di Maio <paola.dimaio@gmail.com
>>>             <mailto:paola.dimaio@gmail.com>>
>>>             *Reply-To: *Ontolog Forum
>>>             <ontolog-forum@googlegroups.com
>>>             <mailto:ontolog-forum@googlegroups.com>>
>>>             *Date: *Saturday, April 18, 2020 at 4:18 AM
>>>             *To: *Ontolog Forum <ontolog-forum@googlegroups.com
>>>             <mailto:ontolog-forum@googlegroups.com>>, W3C AIKR CG
>>>             <public-aikr@w3.org <mailto:public-aikr@w3.org>>
>>>             *Subject: *[ontolog-forum] Catalog of Biases
>>>
>>>             This is a very good find for me
>>>
>>>             https://catalogofbias.org/biases/
>>>
>>>              and hopefully also for fellows on the lists
>>>
>>>             I am researching bias as a pathology resulting from poor
>>>             knowledge modelling, the remedy is
>>>
>>>             knowledge representation
>>>
>>>             It happens to be structured as a taxonomy, what fun
>>>
>>>             PDM
>>>
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>>>

Received on Thursday, 23 April 2020 01:19:21 UTC