- From: Paola Di Maio <paola.dimaio@gmail.com>
- Date: Wed, 22 Apr 2020 07:58:11 +0800
- To: Owen Ambur <Owen.Ambur@verizon.net>
- Cc: W3C AIKR CG <public-aikr@w3.org>
- Message-ID: <CAMXe=Sq2K7Jed4CVGBz-QwRx32nA=E_4H_uOiSUXvmyEQFUeHg@mail.gmail.com>
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> 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> > 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 >> >> >> >> -- >> All contributions to this forum are covered by an open-source license. >> For information about the wiki, the license, and how to subscribe or >> unsubscribe to the forum, see http://ontologforum.org/info/ >> --- >> You received this message because you are subscribed to the Google Groups >> "ontolog-forum" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to ontolog-forum+unsubscribe@googlegroups.com. >> To view this discussion on the web visit >> https://groups.google.com/d/msgid/ontolog-forum/CAMXe%3DSo%2B%3D1X3A4VGN6Ecv78MD604vWRU7600oimG3jDr0fsLtw%40mail.gmail.com >> <https://groups.google.com/d/msgid/ontolog-forum/CAMXe%3DSo%2B%3D1X3A4VGN6Ecv78MD604vWRU7600oimG3jDr0fsLtw%40mail.gmail.com?utm_medium=email&utm_source=footer> >> . >> >> -- >> All contributions to this forum are covered by an open-source license. >> For information about the wiki, the license, and how to subscribe or >> unsubscribe to the forum, see http://ontologforum.org/info/ >> --- >> You received this message because you are subscribed to the Google Groups >> "ontolog-forum" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to ontolog-forum+unsubscribe@googlegroups.com. >> To view this discussion on the web visit >> https://groups.google.com/d/msgid/ontolog-forum/CD63D594-3C23-42D1-BFDD-6D3A383FC126%40if4it.com >> <https://groups.google.com/d/msgid/ontolog-forum/CD63D594-3C23-42D1-BFDD-6D3A383FC126%40if4it.com?utm_medium=email&utm_source=footer> >> . >> >
Received on Tuesday, 21 April 2020 23:59:02 UTC