Problems with Technology for Good

Updated: Apr 25

Status Quo: A display of SISP


The current attempts to use technology to solve the world's problems tend to follow the principles of SISP. SISP stands for solution in search of a problem. This paradigm attempts to find problems to solve with a specific solution already in mind.

Imagine the typical data science consultant. It looks around for big problems to solve with machine learning. The solution is known. The problem is not. After making an inventory of potential problems, the data scientist chooses a problem that fits well with a solution. Very often, the data scientist has little knowledge of the problem and the domain knowledge of those currently attempting to solve it. Oftentimes, the created solution does a cool trick but does not help in effectively solving the problem.

Technology for good initiatives are the analogical equivalent of a construction worker whose only tool is a hammer. This construction worker is running around the construction site looking for places in which a hammer could be a potential solution. Every time upon the finding of a construction task, he asks himself whether the hammer could aid in the completion of this task. Without taking the time to study the problem or looking for alternative solutions he starts hammering.

“If the only tool you have is a hammer, you will start treating all your problems like a nail.” - old proverb

The tech for good landscape is filled with projects that do a nice trick, but do not effectively contribute to solving a problem. In order to change this we prefer an opposite strategy, which starts with the problem.

Unintended Consequences in Complex Systems


First, let me introduce you to the concept of complex systems. Complex systems are systems with many parts that are interacting with each other. A social network, traffic flows, the environment are all examples of such complex systems. Many of the worlds biggest problems are part of complex systems. From starvation to climate change. Interventions in complex systems can often result in negative unintended consequences. Let's start with a short example.


In many parts of the world new ride sharing services are introduced. The services are often branded as a green solution, reducing carbon emission in cities. Governments are hoping that people will ditch their cars, and use their cleaner and often electric cars instead. This concept was expected to reduce general carbon emission, and improve traffic jams in the city. However, in reality this concept hardly ever works. This is the case because the users of the service are often users of public mass transportation. This is an example of how an intervention in a complex system can have a negative impact, when a positive imapct was intended.

We see these examples in technology for good all to often. This shows that good intentions are not enough. In order to analyze the behavior of the system we propose the usage of system thinking to analyze these unintended consequences in these systems.


System Thinking: An approach towards understanding complex systems

Systems thinking is a holistic approach to analysis that focuses on the way that a system's constituent parts interrelate and how systems work over time and within the context of larger systems. The systems thinking approach contrasts with traditional analysis, which studies systems by breaking them down into their separate elements. Systems thinking can be used in any area of research and has been applied to the study of medical, environmental, political, economic, human resources, and educational systems, among many others.link

Systems thinking provides you with a set of tools to understand the behavior of a system, and lets you analyze the effects of interventions. Systems thinking is often being done in collaboration with stakeholders and domain experts. We propose the usage of systems thinking to analyze complex problems and find meaningful solutions based on a thorough understanding of the problem. We also use systems thinking to analyze unintended consequences in the solutions we create.

Wide range of Tools and Going Beyond Glorified Hobby Projects


Most technology for good projects are nothing more than glorified hobby projects. Engineers building a solution with little knowledge of the problem. They often work on it for a weekend. Although this happens with the right intentions, the chance that it will actually lead to real change is slim to none.

Within our founding team we have a wide range of skills. Just to mention a few:

1. Data Science

2. Machine Learning

3. Sensor and Satellite Data

4. System Thinking and Modelling System Dynamics

5. Simulation Modeling

Our goal is to increase this list of skills. We believe that many technological solutions exist at the intersection of these disciplines. Besides that, it also allows us to escape the SISP mindset. SISP is mandatory if you only have one tool. By having a wide range of tools, we are capable of starting from the problem. Secondly, we always analyze the existence of unintended consequences of our service.

Built-In Sustainability


Any company or charity that needs external funding to survive is inherently unsustainable. A true sustainable company is one that delivers valuable products to companies and people, gathers revenue from these activities and delivers social good as an intended side product. We are often combining a financial good with social good. For example, with workspace modelling we help companies be more productive and open sooner and at the same time improve worker safety. With energy modelling we help companies and consumers reduce their energy bill, but also reduce their carbon footprint. We combine a personal benefit with a societal benefit. This way we are capable of being a for profit for social good company.


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