Prejudices in facial recognition and recruitment systems. Accidents involving self-driving cars. This type of failure shows that much remains to be done in the development of AI. The fastest way of moving that development forward is for AI and people to work closely together.

Let us not deny that there have been some major success stories thanks to artificial intelligence. For example, there are AI systems that are able to lip read or recognise tumours, with the help of deep learning. But AI systems also regularly slip up, and that can have very serious consequences. This is especially the case with ethically sensitive applications or in situations in which safety is at stake.

It is therefore time for the next step – a closer partnership between AI and people. This will enable us to develop AI systems that can assist us when taking complex decisions, and with which we can work enjoyably and safely.

AI still faces a stiff learning curve

To start with, AI has a tendency to unquestioningly replicate human prejudices. This is most particularly a problem if social and ethically sensitive applications are involved. Examples include the recruiting of new employees or predicting the likelihood of a delinquent lapsing into crime.

Another case in point is that of opaque decision-making processes. AI systems have to learn to be more transparent. This is certainly true if they are to be used in sensitive contexts, such as law enforcement or detecting social security fraud.

AI systems must not be rigid. They have to adapt to their users and to the changes in society, without losing sight of ethical and legal principles.

Finally, data analysed by AI may be private and confidential. This is especially the case when businesses and organisations operate together in a decision-making system.

What artificial intelligence still needs

TNO is currently investigating what is needed to achieve a responsible decision-making process between people and machines. There are four points to consider:
1. Responsible AI: by incorporating ethical and legal principles into AI.
2. Accountable AI: by enabling different types of user to understand and act upon advice and recommendations given by the system.
3. Co-learning: by adapting, with the help of people, to a changing world. This should be done in a way that ensures that ethical and legal principles are anchored in the system.
4. Secure learning: by learning from data without actually sharing them with other parties.

The aim: reliable and fair AI

TNO is helping to bring about reliable AI systems that clearly operate fairly. Always. And that includes complex and fast-changing environments. We are seeking to develop AI systems that can explain to different types of user why they take a particular decision.

Delving deep with use cases

It’s perhaps also useful to know that, in addition to this area of research, we are also working on safe autonomous systems in an open world. In other words, we are exploring solutions to today’s challenges facing AI from a range of perspectives. But the focus here is very much on ‘responsible decision-making processes between people and machines’. With the help of the following use cases, we are trying out our AI technology in practical situations:

- Secure learning in money-laundering detection
- Fair decision-making in the job market

Would you like to contribute a use case for this programme line?

Then please contact Cor Veenman


Governing privacy and ethics in AI

The expected societal impact of AI is considerable. Think of the application of AI to recruit employees, where algorithms may be used to select employees. However, some AI applications in this field are... Read more

Fair, transparent and trustworthy AI

Current AI systems are far from perfect and make decisions that are difficult to understand. Acceptance of AI systems in society demands transparent algorithms and compliance with legislation and regulations.... Read more
Our work

Autonomous systems in the real world

Robotics, image recognition, self-driving cars. In recent years, artificial intelligence (AI) has brought about some major advances in these fields. Now, though, the safety of such autonomous systems is... Read more
Our work

Artificial intelligence makes money laundering that much harder

In their fight against money laundering, banks can benefit if they are able to exchange data with each other. But how can that be done without affecting the privacy of their clients? TNO is currently... Read more
Our work

The right people for the right job: an accurate matchmaker based on AI

Diplomas do not tell the whole story – not by a long chalk. What matters much more nowadays is the skills that a person has, and how relevant those skills are in relation to what is described in a vacancy.... Read more

Cor Veenman

  • Machine learning
  • Data science
  • Responsible data science
  • Fairness
  • Bias