Re: [ontolog-forum] Catalog of Biases

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>
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 <http://www.if4it.com>1.908.294.5191 (M)*
>
> *Guerino1_Skype (S)*
>
>
>
>
>
> *From: *Ontolog Forum <ontolog-forum@googlegroups.com> on behalf of Paola
> Di Maio <paola.dimaio@gmail.com>
> *Reply-To: *Ontolog Forum <ontolog-forum@googlegroups.com>
> *Date: *Saturday, April 18, 2020 at 4:18 AM
> *To: *Ontolog Forum <ontolog-forum@googlegroups.com>, W3C AIKR CG <
> 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 Tuesday, 21 April 2020 02:27:18 UTC