- From: Owen Ambur <Owen.Ambur@verizon.net>
- Date: Wed, 22 Apr 2020 15:20:45 -0400
- To: W3C AIKR CG <public-aikr@w3.org>
- Cc: Chris Fox <chris@chriscfox.com>, "Jorge Sanchez." <jorgesr@zoho.eu>
- Message-ID: <4403dcd9-e22a-0710-873d-4d4b247a7a31@verizon.net>
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 >> >> -- >> 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 >> <mailto: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 >> <mailto: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 Wednesday, 22 April 2020 19:21:09 UTC