Conclusions

• Author: Cepilovs, Aleksandrs
• Pages: 32-33

32  AI IN THE OPERATIONAL MANAGEMENT OF LARGE-SCALE IT SYSTEMS

organisation, as well as an important step towards a more environmentally sustainable organisation.

5. Conclusions

Over the last decade we have observed a very rapid pa ce of advancement of AI in terms of both theoretical

developments and applications  and it is unlikely to slow down any time soon. This rapid development as well

as the pervasiveness of AI as a new technology also means that it has already profoundly affected organisations

and societies, with implications for many of our spheres of life. Like many new technologies, regulation is

lagging behind and therefore often cannot address many of the pressing concerns, inclu ding the ethical

implications of AI, which are much cited in particular when AI is used for automated decision making. These

concerns are valid and need to be appropriately addressed. However, they should be addressed in a way that

does not hinder the development of AI capabilities in the EU. It is therefore paramount to adopt a strategic

approach to regulating AI in the EU. This appro ach should allow for sufficient flexibility for the development of

new technology and its application across the economy, while at the same tim e ensuring that whenever AI is

deployed where it may have direct implications for human beings, it must be transparent, auditable and

compliant with all applicable legal and ethical requirements.

For eu-LISA, as for any other organisation providing IT services implementation of AI is not a question of if

but w hen and to what extent The EES and ETIAS both foresee a certain level of artificial intelligence or

automation and will therefor e have an immediate effect on individuals. Considering that the precision of AI

systems based on machine learning algorithms depends to a significant extent on data sets used for training,

and the quality of data sets used for the training of biometric recognition systems to be used in the EES will to

a significant extent determine the quality of the AI systems. With this in mind, a careful evaluation of the trade-

off between privacy prote ction and system performance will need to be made, in particular focusing on the

benefits and disadvantages of using real data sets for ML system training.

There are, however, many applications where AI can be deployed without any implications for human beings.

This report has therefore largely focused on applications where ethical and legal c onsiderations are either not

relevant or are secondary; namely on those applications where AI can improve the performance of eu-LISA

either by improving the quality of service (e.g. chatbots or virtual as sistants) or the performance of the

infrastructure (e.g. automated failure detection), or by reducing costs and improving performance by taking

over some of the repetitive routine tasks (e.g. AI on IT service desks).

Although AI has great transformative potential, it does not come without challenges and risks. Therefore,

organisations considering the depl oyment of AI in either internal operations or services should take th e

following factors in consideration:

 Development of machine -learning based systems will require large data sets in order to train the ML

algorithm. In order to make sure that the AI system performs with a relatively low le vel of errors, the

training data sets need to be of very high quality. Creation of high quality data sets will require significant

initial investment.

 Development and deployme nt of AI based on machine learning (in particular deep learning neural

networks) algorithms require substantial computational resources, which are normally not available within

one organisation. Therefore, the development and deployment of AI external service providers, such as

Amazon Web Services or Microsoft Azure, or shared infrastructure, such as the EuroHPC, will be necessary.

 AI systems  both rules-based and ML-based  are often not static, as they operate in a continuously

changing environment. In order to take into account the changes in the environment, AI systems must be

continuously update d to r emain accurate. This will require either updating the code of the r ules-based

systems, or retraining the ML algorithms with new da ta sets or using the more recent continuous training

approaches. This has direct implications for the procurement of such systems, as well as creating a