Meaningful human control in defence applications
As computers become increasingly smart, they are able to take on more and more tasks from people. At the same time, they are becoming more capable of independent decision making. The big question is how do we – people – make sure that we retain meaningful control?
AI as a repressor? Well, we have not reached that stage just yet. But in many countries all over the world, there are police teams experimenting with predictive algorithms in their detection work. In September 2020, Amnesty International published an alarming report on the subject. It concluded that these AI systems acted in a discriminatory manner that was not apparent to people.
As objectively as possible
So for a computer, it is not easy to filter out human prejudices from the available data. And that is a big problem. After all, if someone is identified as a possible suspect on the basis of an AI prediction, this should happen in an objective a way as possible, and not be based on any kind of preconception.
AI systems as team partners
The solution? Put together teams in which AI systems partner people. This means that when it comes to critical decisions, people remain involved and in control. Such interaction between people and AI is essential for developing systems that are able to act ethically on the basis of predictions.
In doing so, people can use the capacities of advanced AI to the best-possible effect by delegating as many tasks as possible at an abstract level. Meanwhile, there is always the option of making operational adjustments. At the same time, AI systems have to be able to explain themselves and actively involve people with the data analysis process. This way, we can prevent AI from drawing conclusions based on unreliable data or erroneous assumptions.
Greater focus on interaction between people and robots
A 'social AI' layer is therefore needed. Working in partnership with the Dutch Ministry of Defence, NATO, and various universities, TNO has already made a number of highly promising advances in this area. For example, there is a prototype of a delegation system that is already being used with interactions between people and robots. We also have a test in which we are able to measure (in a laboratory setting) the degree of human control.
Final responsibility rests with people
The aim is to ensure that people are able to entrust tasks to AI systems in a responsible manner without losing control. In other words, it will still be people who bear final responsibility. There are currently a great number of fields that are ripe for this type of ‘controllable’ AI system. Examples include the police, defence, and the logistics and care sectors. Each of these areas would benefit hugely from AI systems that take over various tasks in a reliable way and without any preconceptions.
Christopher BrewsterFunctie:Senior scientist
Christopher Brewster is a Senior Scientist in the Data Science group and Professor of the Application of Emerging Technologies in the Institute of Data Science, Maastricht University. His research has focussed on the application of Semantic Technologies, Open and Linked Data, interoperability architectures and Data Governance, mostly to the food and agriculture domains.
Daniël WormFunctie:Senior consultant
Jok TangFunctie:Deputy Research Manager Data Science
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AI Systems Engineering & Lifecycle Management
The AI system for the future. At TNO, we work on AI systems that remain reliable and can handle new functions in the future.
You can read about how AI is educated in Chapter 1. How can we make clear to AI which goals we want to pursue as humans? Andhow can we ensure intelligent systems will always function in service of society?
Innovation with AI
What does that world look like in concrete terms? Using numerous examples, TNO has created a prognosis for the future in Chapter 2. Regarding construction, for example, in which AI will be used to check the quality, safety, and energy efficiency of buildings before they are actually built. Or healthcare, where robots will partly take over caregivers’ tasks and AI will be able to autonomously develop medicines.
Innovating with innovation AI
How AI will change research itself is explained in Chapter 3. For example, what role will AI be permitted to play in knowledge sharing? And what will happen when we make machines work with insurmountably large data sets?
David Deutsch on the development and application of AI
Peter Werkhoven, chief scientific officer at TNO, joins physicist, Oxford professor, and pioneer in the field of quantum computing, David Deutsch, for a virtual discussion. Deutsch set out his vision in 1997 in the book, The Fabric of Reality. Together, they talk about the significance of quantum computing for the development and application of AI. Will AI ever be able to generate ‘explained knowledge’ or learn about ethics from humans?