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
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Frank Guerino, Principal Managing Partner
The International Foundation for Information Technology (IF4IT)
http://www.if4it.com
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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.
To view this discussion on the web visit https://groups.google.com/d/msgid/ontolog-forum/CD63D594-3C23-42D1-BFDD-6D3A383FC126%40if4it.com.
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
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