Kalle reflects on good vs. bad data for AI assisted FSSD

How are good vs bad data managed as our AI engine collects data for FSSD support?

The short answer is that the FSSD Global’s Operative system “ABCD in Funnel” is an absolute code for systemic, systematic and strategic sustainable development. Either the code of the Operative system is violated at the global level or not. Applying the ABCD-in-Funnel as a lens for collection of data, is the same as your own brain applies when it gathers data under A, B, C and D respectively. This is the first line of defense from bad data.

The FSSD also offers a next line of “defense” from bad data. Scientists, business leaders within the Swedish Stepwise group and other experts have performed ‘ABCD-in-Funnel’ “sector-analyses” as if the global energy-, traffic, agriculture-, forestry-, material-flow-, infrastructure-, HR- sectors were “organizations”. All to collect and feed data into the AIs search engine. For an organization to learn what is scaleable on the sector it relies on, is naturally highly relevant. If an idea is doomed for a whole sector, it’s projected life span for an individual organization is naturally very risky.

Finally, AI data is also collected from peer-reviewed case studies where organizations from the public and private sectors across the globe have found innovative solutions to their respective problems.

More in detail

One of the major questions we get at FSSD Global is how good data vs. bad data are managed by our AI engine for FSSD support.

The short answer is that the FSSDs Operative system “ABCD in Funnel” is an absolute code for systemic, systematic and strategic sustainable development. “Absolute Code” here means that the Operative System helps you ask the right, crystal clear, open questions. And respond to them if there is only enough competence in the room. So, the code serves as a first filter to get rid of bad data and suggest good data on what is sustainably scalable in the future. “Either matter from the Earth’s crust has stopped increasing systematically in Nature in such futures, or not (first boundary condition)”. And so on for the rest of the 8 boundary conditions within the code.

The same goes for the individual organization, either it contributes to violating the boundary conditions, or not. Either it gradually and strategically decreases such “bad” contributions, while innovatively increasing “good” sales of products and services that are scalable within the absolute boundary conditions. Or not.

So, the FSSD code itself is a response to the question in the title, and is used by our own brains, alone or in communities, already before our AI engine kicks in as support.

Now, what about context? Isn’t it possible to “play sustainability chess” for instance by sacrificing some pieces, would that be good for the big picture of reaching the boundary conditions of “sustainability checkmate”? For instance chosing “bad” short-term solutions that are possible to use as smart intermediate steps towards success? Or perceiving a measure as sustainable by underestimating its possible scaleability in the future? For instance launching a chemical as “degradable”, which it may not have time to be if used and leaked to natural systems in large enough quantities. This is, in fact, a very good question that the ABCD-in-Funnel is designed to deal with.

So, there is also a next line of more contextual “defense” from bad data here. Scientists and experts have performed ‘ABCD-in-Funnel’ “sector-analyses”, as if the global energy-, traffic, agriculture-, forestry-, material-flow-, infrastructure-, HR- sectors were “organizations”. Thereby offering more detailed data about many possible vision-combinations for respectively (“A”), current situations for respective sectors in that context (“B”), possible measures towards such ends (“C”) and prioritizations of such (“D”). This includes modelling, also mathematical such, to calculate the scalability of various proposed measures. So, organizations active within those global sectors, and applying AI assistance of their ABCD workshops, can also get real-life concrete proposals as creative inputs for their respective ABCD workshops.

Finally, AI data is also collected from case studies, performed in action research with scientists, where organizations from the public and private sectors have found clever solutions to their respective problems. Or in other words, there are role models of FSSD management from myriad organizations, where scientists have contributed data through the AI engine as creative support to the individual user.

The FSSD Global is there to make it clear, for instance under moderating of ABCD workshops or using the Platform for consulting/sounding board activities, how scalable ideas are under A, B, C and D respectively. Financially this corresponds to low vs. high risk, excellent vs. doomed innovations etc.