Watch the recordings from our ongoing open talk series on Public Interest AI.

Open talks

We've asked our community: what is Public Interest AI? Watch their answers and add your own short video clip.

Voices on PIAI

Our research sheds lights on many interesting questions around PIAI. All blog posts from our team can be found on the Digital Society Blog.

Blog posts

We've put together a repository on Public Interest AI resources. From helpful scripts to detailed readings.

Knowledge base

We believe it is crucial that an AI-application is justified if it is to serve the public interest. To serve a public, this public must be able to get informed and involved. A justification, as we understand it, should make transparent how the AI application addresses the challenges it is designed for and why this AI system is the best way to do so. A justification should be public (or publicly accessible) and go beyond individual commercial interests.

Often, an AI is built to solve a problem. We believe that the people who made the AI should explain to everyone why using AI is the best way to solve the problem. We call this condition “justification”.

Justification

Often, the principles related to equity in the AI-context are enhancing fairness and reducing bias. But equity should not be reduced to that: More fundamentally, a question that should be asked is whether the AI system is truly necessary and, in addition, how the system is located in current power relations – for example whether it impacts vulnerable groups negatively. Additionally, it needs to be clear that an AI system without bias does not automatically mean that it serves the public interest – think of a social media algorithm that works just as intended, but nevertheless increases inequality through misinformation spreading.
If AI is to succeed in supporting equity amongst all citizens, the design process should promote inclusive principles: First, equity in access is necessary, meaning especially open systems and open source where it is possible. Second, equity in knowledge should be promoted, meaning the civic tech literacy should be strengthened. Third, the equity between generations should be considered as well, especially regarding the impacts AI systems can have on the future.

Equity means for example fairness. An AI should be fair and not treat certain people differently or unfairly. An AI system can support equity only if it is inclusive. “Inclusive” means that everybody can use it. If an AI is good for equity, everybody should be able to access and know about it. We call this condition “equity”.

Equity

A critical aspect of an application serving the public interest is that there is a certain “public” defined that has a stake in the development process. No single entity &#8211; no single person or organisation &#8211; can define what the public interest is. We believe that a deliberative development process must be encouraged to achieve this. Otherwise, it is very likely that the interests of “the public” will only be assumed, but never actually met.
There are various paths which a deliberative process can take: From transparent access to information, surveys, design research, co-design approaches, ethics boards or regular consultations with engaged citizens &#8211;  many approaches are possible.
Of course, participatory design is no guarantee for a democratic process, or a working product, not even a guarantee to serve the public interest. For example if forms of “participatory washing” allow only very exclusive participation and the process is getting inscrutable instead of opened up, “participation” can be misused.

An AI serves the public interest if it is good for the people living in the society. To make sure that this is so, it is important to ask the people what they need. This is called “participation”. We believe that this is necessary. If this does not happen the people who make the AI can only assume what the people need. But they never know it for certain and could forget special needs. There are different ways to ask people what they need. You can for example do a survey or have meetings with engaged citizens. 
We call this condition “participatory design”.

Participatory design / Deliberation

A technical system that does not meet basic safety standards cannot serve the public interest. There are three distinct areas of technical safety measures.
First, the data quality and system accuracy must be considered. It is necessary to provide transparency about the data sources as well as their exact use and documentation of their inadequacies. Biases and incorrect outcomes can disqualify an application from truly serving the  public interest.
Second, data privacy is necessary, as privacy is understood as a prerequisite for the realization of an autonomous life.
Third, the overall system safety and security is important. As a starting point, failures should be monitored to ensure efforts are put in the right spots. In general, the system needs to be designed in a safe and robust way, so that, for example, malfunctions endanger neither public safety nor security.

An AI needs to be safe. This means for example that it must be clear if the AI does the right things. This is called “system accuracy”. Also, it is important to notice when the AI does not work, so that the AI can be made safer. We call this condition “technical standards”.

Technical standards / Safeguards

As seen with the other conditions, the consideration of an application as serving the public interest does not end once an application is deployed but is an ongoing process. This is especially true for this fifth condition which states that an AI application needs to be open for validation by others.
This is the case because transparency is necessary, first, to avoid unintended societal harms, such as the perpetuation of existing stereotypes. Second, openness for validation is understood as a fundamental democratic norm, as civil society does not only have the right to understand how technology works, but also needs to be able to validate and confirm its mechanisms.
Finally, this is closely related to accountability, which is understood as a clear attribution of responsibility and liability. Thus, openness for validation indicates a direct feedback channel for citizens to those entities which are responsible for the AI-application and have the possibility to implement changes or terminate the use of the application.

We believe that all people should be able to find out details about the AI. This means that they can check if the AI works correctly. This is important because it could be that an AI does not work correctly and causes harm for people. If there is no possibility to check and report on that, the AI would not serve the public interest. We call this condition “open for validation”.

Open for validation

As AI does not exist without consequences but quite materially and as its applications have tangible ecological, social and economic impacts, it is an important aspect of the development of an AI-solution to keep its sustainability in mind. Not only can this include ecological decisions which can have an impact on the CO2 emissions – as for example which model is deployed, where, how and when it is trained, and for which use cases it is made – but also questions of social and economic sustainability, which might tackle questions like how does this application impact working conditions. Economic sustainability should not be understood as serving private commercial interests, but as the ability of an AI project to sustain itself. Another question spanning all three fields of ecological, social and economic sustainability is the question of interoperability and the reusability of models and data, which are key factors to sustain small AI projects and to reimplement them for example in other domains as well.

An AI is a programme running on a device – for example a computer. This needs electricity to run. Producing electricity can cause pollution. AI can therefore have impacts on nature and people. If an AI should serve the public interest, it is important to remember and take care of that. We call this condition “sustainability”.

Sustainability

This website is a space to share knowledge and foster collaboration on AI in the public interest. The research that backs the content of this website is undertaken at the AI &amp; Society Lab of the Alexander von Humboldt Institute for Internet and Society (HIIG) in Berlin in cooperation with a wide group of experts. Visit our project website to find out more about our research and other related content.

Who's behind publicinterest.ai?

Besides this knowledge resource, we’re showcasing a handful of projects on Public Interest AI. This toolbox is a work in progress and open for ideas and collaborators. So come back once in a while to see what’s new and contact us if you want to contribute with a tool.

The publicinterest.ai toolbox

We understand Public Interest AI (PIAI) systems to be those AI systems that support those outcomes best serving the long-term survival and well-being of a social collective construed as a “public”.
Our understanding is inspired by theorists like Barry Bozeman and John Dewey and many other sources in the tradition of public interest theory. In our research we aim to make these ideas useful for the discussion on AI and how it could serve people and equity in societies instead of private goals and profit maximisation.
Public interest is closely entangled with the idea of democracy and the rule of law. Developing a collective idea of the public interest regarding a certain issue is an established process in many societies and has a long tradition in legal discourse. The public interest is never universal and instead needs to be defined for each societal issue by a deliberative and participatory process. To act in the public interests citizens think about more than their private interests &#8211; they think and act for a common good.
In our research, we explore and discuss what this understanding of the public interest means for the development and implementation of AI.

AI means artificial intelligence. This is a special way to solve problems with a computer. We believe that it is important to learn more about uses of AI that are good for people. We call that Public Interest AI, or PIAI for short. Public Interest means that the AI serves the well-being of the public. The term “well-being” describes the happiness and health of the public. The “public” means all people who live in a community together.
To act in the public interest means for example that people think about what everyone needs and not only about their individual interests. “Individual interest” is a phrase which means: something that may only benefit one person. The opposite of that is “public interest”: this phrase describes something that may benefit many people.
We explore what “public interest” can mean for AI projects. An example of “Public Interest AI” is a system that removes litter from the oceans.

What is Public Interest AI?

We believe that besides legal requirements informed by ethical considerations certain conditions are important for the process of development and implementation of AI systems to serve the public interest.

It is not easy to decide if an AI serves the public interest. We found six conditions that help to decide this. “Conditions” are like questions: The people who make the AI need to answer them to find out if their AI is in the public interest. You can find out more about them below.