Re: Catalog of Biases

Dear Paola and all members of the respective listserv lists,
The link is definitely useful. However as I pointed off-list to you and Carl Mattocks, my hands are tied. I am one of ten individuals in my country having to build the entire infrastructure for science, technology and innovation from scratch, now that our 90% tourism based economy has collapsed completely. Standard and Poor's has identified my native country of Aruba as the single most devastated economy of 162 countries surveyed globally.
I am trying to focus on eGovernance, eGovernment and the use of StratML and how to utilize AI for Travel & Hospitality in the post-COVID-19 world.
I will be able to look into this in a month from now.
It would help if someone would assist in providing some general literature on bias categorization across the broad spectrum of sciences.
regards

Milton Ponson
GSM: +297 747 8280
PO Box 1154, Oranjestad
Aruba, Dutch Caribbean
Project Paradigm: Bringing the ICT tools for sustainable development to all stakeholders worldwide through collaborative research on applied mathematics, advanced modeling, software and standards development 

    On Tuesday, April 21, 2020, 5:02:15 AM ADT, Paola Di Maio <paoladimaio10@gmail.com> wrote:  
 
 MiltonI wonder if youd be up for translating /mapping to KR the debiasing algos currently in usehttp://aif360.mybluemix.net/  

This would be a valuable deliverable from usP
On Tue, Apr 21, 2020 at 12:11 AM ProjectParadigm-ICT-Program <metadataportals@yahoo.com> wrote:

Bias can result from poor knowledge modeling, but IMHO when we conduct scientific research bias arises from (1) the scientific method domain of research specific implementation, (2) instrumentation bias, both in (i) technical, (ii) data recording, (iii) significant numbers of data, (3) observer caused bias where the mere observation itself causes a perturbation in the observed system.
The resulting knowledge modeling bias can only be corrected if the qualitative and quantitative aspects of (remote) sensory input are fully understood.
Here is where neuroscientists, cognitive scientists, psychologists, philosophers and physicists come in.
There is no SINGLE knowledge representation scheme. But only categories of knowledge representation.
We can use AI and category theory to find which categories of KR are most suited for each domain of scientific discourse.
For each well established category knowledge modeling bias can then be corrected by appropriate KR schemes.

Milton Ponson
GSM: +297 747 8280
PO Box 1154, Oranjestad
Aruba, Dutch Caribbean
Project Paradigm: Bringing the ICT tools for sustainable development to all stakeholders worldwide through collaborative research on applied mathematics, advanced modeling, software and standards development 

    On Saturday, April 18, 2020, 5:19:26 AM ADT, Paola Di Maio <paola.dimaio@gmail.com> wrote:  
 
 This is a very good find for mehttps://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