I’ve been attending a very fully scheduled, UN-led humanitarian partners get-together with participants representing billions of dollars, affecting millions of lives, and decades of effort.

I have heard calls for “new ways to structure relief”, and descriptions of innovation that seeks to collect lessons learned. My reaction here is admittedly full of ignorant generalization, but the inspiration for my Has-Needs concept really hasn’t changed since 2010.

Everything, everything I have heard is just shades of the status quo. Instead of regional information management, I hear “let’s include the local organizations”. When asking about empowerment of local citizens in a security framework, I actually received “I’m not sure I understand the question.” from a panel of practitioners. This echoes conversations I have had over the past 15 years.

Meanwhile all of the existing issues don’t change a bit. Questions like, “How do we normalize between different databases?” “How do we share information so that co-responders don’t step on toes and create bad blood between organizations?” and “How do we use location data and expose it without endangering civilians?”

All in all, no new problems and worryingly, no really new solutions. Just fear generated by the Trump regime turning off the spigot of cash.

A well paid vanguard are now using LLMs and RAG (conversational AI) to sort through their own data, and data that was collected from years of work and cross pollination.

Tools like Neo4j are touted as nice places to be a repository of information, once it’s all formatted and collated and entered correctly. While Neo4j has ways of intuitively displaying data, it is up to the user to formulate a question that pulls useful data from the structure to view. So, not very intuitive at all, in practice.

The innovation being offered?
Creating databases that can have vectors added later. Which is useful.

Where to begin… Obviously, I feel that the Digitally Sovereign Human is the answer to everything. All of the problems being encountered are solved completely by putting all of the attention and resources toward empowering ‘the least among us’ which, at its essence, is a naked human being who is able to breathe and communicate. In short, us all.

I’ll try to tackle the issues/answers in a way that might fit into the institutional pattern better. Apologies in advance if it gets obscure, boring or seemingly irrelevant. I do want to emphasize that nothing I am discussing here is impossible.

There are small details that matter and I’ll get to those, namely the actual software solution that maintains security between one’s public interactions and their ‘core wallet’ – along with protected location data. The ‘core wallet’ (home address, effectively) and user location, are sacrosanct in any effective human or humanitarian solution, and those are so basic as to be purposefully coded into my designs.

Issues that need to be addressed:

These are all intertwined and part of the reason I say Has-Needs is emergent by design. Solutions emerge from use of the system.

1. Organizations would love to be able to track donated funds as they trickle through a society. Then we’d all know right away which donations did the most ‘good’ toward an end goal. We’d also know when any of the funds were used by bad actors. Laudable goals!

Crypto offers the means to directly tie an amount of money to a digital asset that cannot be changed or deformed. It is possible on the Cardano blockchain for instance, to generate a wallet and create an asset that can be securely broken into pieces within a few seconds.

An analogy is an aid organization buying a lump of gold and giving it to a user, who then breaks off chunks to spend for whatever. Each chunk has the original owner’s ID stamp and reports back home when it is exchanged, transferred, or turned into cash. This is all easily doable with merely a phone app, today.

2. Organizations struggle with ethical and other concerns when gathering and storing data on human beings. Location matters on many levels and if the database is ‘open’ then the location data exposes humans to possible danger. It is hard to create meaningful solutions if you don’t take location into account.

Many organizations do not see that the common factor is groups of outside people gathering data from ‘on high’ about people ‘down below’, pulling in certain facts in order to do good ‘for’ the recipient of aid.
This is inherently colonialist and serves to perpetuate disability.
So, what’s the alternative?

Has-Needs provides the individual human a means to selectively share details with anyone, in the context of a value exchange, even if the value is delayed or not immediately obvious. Each detail is provided in a Smart Contract designed to perform certain tasks. One task is to keep the data accountable. For instance, a Smart Contract, in the form of a “token” can be in effect for a certain length of time, using the data for a specified purpose, and then delete its contents at a specified point.

This “token” would accompany the data, and as part of a protocol (a set of rules for moving information), it would prove that the data was given freely by an owner. The token can be encrypted such that any use of the data requires it to ‘phone home’ to be decrypted, thereby notifying the owner of all recent activity.

Should any of this information ever be found without a token, it could be considered a theft and treated accordingly, using the existing legal structures in place.

Location data is of particular interest for many reasons. However, if the location and it’s associated facts are guaranteed to be accurate and correct, then it can be used without needing to be associated with anything private. In this way, an organization could obtain permission to use personal data, take the useful parts into their database safely, destroy any potentially dangerous parts, and everyone can benefit while being assured of validated and accurate information.

It is also possible through homomorphic encryption to understand an approximation that is useful without knowing the exact detail. With location, this is useful so a data pool could be searched to discover what things are nearby each other, using their encrypted form that nobody can read.

3. Accountability. How can we tell what is working and what is not?

At the governmental level, accountability comes from transparency. Some body or agency is tasked with examining the records and determining if everyone did what they were supposed to.

If, on the other hand, every public request is part of a permanent record, then it would be easy to tell who is being responsive just by counting the incomplete requests. Citizens would be able to see who is getting things done, and governance could see which departments are in need of praise or attention. When it comes time to vote, citizens would know right away who is more likely to be responsive, and governance could determine if there needs to be more staff added.

The same mechanism would be useful in many different applications.

4. Ethical use of AI. If all the records are being used to provide answers, how do we keep private details out of public view?

Let’s reframe how AI is being used. Right now, Large Language Models (LLMs) are used to store chunks of documentation and associate them. An asked question causes the LLM to go through its chunks and provide an output of words in a row. Each word in a response is the most statistically likely one to appear, according to the chunks available.

This is how bias can easily be entered into a system. For instance, if you only use published academic works to feed an LLM, then certain ideas will be described as “fringe” or “pseudoscience” because that’s how academics have evolved to describe certain things. Even if an objective view could equate Dark Matter and Psychic phenomenon as having the same amount of ‘proof’, it is guaranteed that Dark Matter will be represented as valid, and telekinesis will be called fringe.

Has-Needs uses AI to match resources to needs. Every completed value exchange creates a new ‘synonym’ in the system ontology. It’s also known that the synonym is relevant to the location where the exchange took place, so naturally the ontology will be local too. As value exchanges take place throughout a region, a tapestry of associated meaning will emerge. This tapestry reflects cultural and linguistic truth of the people using the system. No personal data is being exposed or examined by the system, but value is being provided through use of AI.

Safe AI can also be used to safely extract information.

In times of desperation, the acceptance of a substitute becomes much more flexible. For example: I ask for beans, but if my family is very hungry, I’ll settle for a bag of flour. As the system evolves, synonyms will correlate closely, and anomalous pairings could indicate when something has gone awry.

Similarly, if a steady regular stream of requests for food should suddenly stop, we could assume a number of things because we know that the human need for food did not change. Who is providing the new supply? Have the citizens started to grow their own? these are questions that might be asked.

Whether dangerous groups have moved in, or local expertise has provided a new resource, these indicators warrant a closer look.

Pattern matching is something AI is very good at. The patterns being matched can tell a wealth of useful information without burdensome data queries over a body of accumulated data.

5. Data collection vs Data providing. Aren’t we all just people in need?

In conversation with practitioners, it is hard to break out of entrenched mindsets. When discussing a solution, they will quickly start thinking of themselves as managing the solution for some group of constituents.

I try to frame the issue in terms of every human having needs and resources. The practitioners need to obtain valuable data to make better decisions, to better help those in need. They are customers of data, in effect. The citizens being helped are, in fact, data providers but they are never allowed to feel the raw power of this dynamic.

Organizations think in terms of sharing different layers of collected information to the degree that my suggestion of including citizens in that process causes confusion. Even when talking with participatory researchers, they revert to thinking of ways to get citizens more involved with decisions coming from collected data. Nowhere have I heard meaningful discussion about putting the individual in control of the process entirely.

If we did this, a number of things happen quickly. First, practitioners become people too, with their own needs and resources to share. The data providers, people in need in this case, are deciding what to ask for and deciding what they will accept. The relationship becomes circular.

This mentality already exists among empowered NGO staff who seek to collaborate and interact as equals. It is novel however for the aid recipient, who then carries on this mentality in other relationships and this is the real value ofHas-Needs. It produces, supports, and reinforces building Networks of Efficacy because everything is couched in terms of value exchange. “My location data is worth a bag of beans even though I have no money.” is not a thought that ‘victims’ of a disaster have been allowed to assimilate even though it is the truth.

Does this disempower the aid organization? No, not at all. They get to become insanely efficient by not having to guess anymore, and all parties benefit from a more resilient populace.

6. Data ownership.

We are living in the legacy of needing to own data. Organizations built expensive infrastructure to store and mash lots of data. Owning this data and selectively sharing it has been seen as a goldmine that could sustain operations. This has turned out not to be true.

From simple logistics to GIS use, we see many examples of what can be done with charts and graphs that impress donors and justify salaries. Departments, agencies and even whole organizations are dedicated to the analysis of data an organization owns and selectively trades with others. I have yet to see an annotation that any data was provided by citizens or owned by them.

I have had conversations with UN Section Chiefs, NGO Directors, and other practitioners in positions of power and they say the same thing: “Our biggest challenge is getting timely, accurate, location enabled data.” They all agree stale numbers on a chart are mostly useless and it is prohibitively expensive to send out PhDs gathering information.

I believe the time has finally come where NGOs understand “pristine” data is more valuable than having ownership and control of it.

The question comes to how individuals can be convinced to provide it. The existing model is roughly “Believe me! I’ll drive my cart through town and you throw your apples on it, not knowing where I’m going or when/if I’ll come back.” Even for something as ephemeral as location data, this doesn’t intuitively feel like a good deal. The results of various GIS experiments bear this out.

On the other hand, when someone is shown that they have control, when permission is asked, and the value directly enumerated, then their interest to participate becomes aligned with achieving personal goals. In this case achieving their goal aligns with the organization goal of connecting resource to need.