NADI offers extensive expertise in the field of artificial intelligence. A number of its members are involved in the AI4Belgium initiative, which brings together academic experts and representatives from government and business to develop AI applications and researchers in Belgium. To cite a few key areas of expertise, E. Tuci is conducting research into enabling bio-inspired robots to operate in complex environments and learn autonomously from their experience. He designs control mechanisms underlying complex behavioral, social, cognitive and communication capabilities, and studies the operational principles of cognition and learning in natural organisms. B. Frénay aims to make machine learning techniques more robust, interactive, interpretable and reliable. Indeed, machine learning is now a widespread approach to solving many data problems, but existing tools are sensitive to anomalies, difficult to understand, hard to control and do not provide sufficient guarantees about their behavior. He is also working with colleagues at NADI on AI in education and AI legislation. W. Vanhoof is developing techniques to automatically check whether a given piece of software implements a particular algorithm. These techniques have several applications, ranging from program understanding to plagiarism and malware detection, as well as advanced analysis and optimization such as automatic detection of parallelization strategies. I. Linden explores how business intelligence platforms can be developed to extract useful information from organizations. She also develops new methods for modeling, acquiring and manipulating weakly structured data such as text and expert knowledge. J.-M. Jacquet designs new programming methodologies for designing programs by declaring what needs to be solved, rather than how to solve the problem in question. This line of research has been supported by Walloon Region projects to produce, for example, ExpeSurf, an expert system for multi-layer engineering, and Seplans, an expert system for estate planning. He is also interested in the design of complex knowledge representation systems to model socio-technological systems. G. Perrouin studies how software testing techniques can be applied to AI algorithms to make their use safer. He also looks at AI applications to complex software systems. J.-N. Colin explores the application of reinforcement learning to smart honeypots.

From a humanities perspective, A. de Streel and H. Jacquemin have studied the legal framework for robots and AI. This work led to the publication of a reference work in 2018. The concept of "algorithmic governmentality" was invented and studied in depth by A. Rouvroy. With N. Grandjean, J. Grosman, C. Lobet-Maris and Y. Poullet, she studies the ethics of artificial intelligence. In her thesis, L. Costa analyzed how privacy induces a new legal approach to emerging digital technologies. Elsewhere, C. de Terwangne, J. Herveg, B. Michaux, Y.Poullet and A. de Streel study data protection, particularly in light of the GDPR, liability, intellectual property and competition law. M. Lognoul, A. de Streel and B. Michaux study the explicability of AI from a legal perspective. W. Hammedi studies the application of AI techniques to customer experience. A. Castiaux analyzes the opportunities offered by AI to develop innovation, the role of innovation eco-systems in its development, and the impact of AI on organizational and societal change.

Recently, as an example of interdisciplinary research, B. Frenay and Y. Poullet were invited to draft, at the request of Convention No. 108 of the Advisory Committee, new Council of Europe recommendations on profiling in the AI era. A detailed report on the subject will be published shortly. NADI is also developing global expertise on the development of AI within public administration.

• A. Bibal and B. Frenay. Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment. 2016 NIPS Workshop on Interpretable Machine Learning in Complex Systems. Barcelona
• A. Bibal and B. Frénay. Interpretability of Machine Learning Models and Representations: an Introduction. In 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, pp. 77-82, 2016.
• A. Bibal, M. Lognoul, A. de Streel, B. Frenay. Implementing Legal Requirements on Explainability in Machine Learning Artificial Intelligence and Law, 2020
• C. Colot, I. Linden and P. Baecke. A Survey on Mobile Data Uses. International Journal of Decision Support System Technology, 8(2), 29-49, 2016.
• L. Costa, Virtuality and capabilities in a World of Ambient Intelligence, Thesis defended at Namur (2015), ~Springer International Publishing, 2016.
• B. Dumas, B. Frénay and J. Lee. Interaction and User Integration in Machine Learning for Information Visualisation. in ESANN 2018 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 97-104, Bruges, 2018.
• B. Frénay and B. Hammer. Label-noise-tolerant classification for streaming data. In Proc. International Joint Conference on Neural Networks, IJCNN 2017, pp. 1748-1755, 2017.
• M. Gianni, K. Gotzamani and I. Linden. How a BI-wise responsible integrated management system may support food traceability. International Journal of Decision Support System Technology, 8(2), 1-17, 2016.
• J.-B. Hubin, H. Jacquemin and B. Michaux (ed.), Le juge et l'algorithme : juges augmentés ou justice diminuée, Coll. du Crids n° 46, Bruxelles, Larcier, 2019, 301 p.
• J.-M. Jacquet, I. Linden, and M.-O. Staicu. Blackboard Rules: from a Declarative Reading to its Application for Coordinating Context-aware Applications in Mobile Ad Hoc Networks. Science of Computer Programming, 115-116: 79-99, 2016.
• H. Jacquemin. Comment lever l'insécurité juridique engendrée par le recours à l'intelligence artificielle lors du processus de formation des contrats, Droit, normes et libertés dans le cybermonde, Liber amicorum Yves Poullet, Bruxelles, Larcier, 2018, pp. 141-172.
• H. Jacquemin and J.-M. Van Gyseghem. Le big data en matière d'assurance à l'épreuve du RGPD, Bull. Ass. dossier 2017, Data Protection: the impact of the GDPR in insurance, pp. 233-260.
• R. Marion, A. Bibal and B. Frénay. 'BIR: A Method for Selecting the Best Interpretable Multidimensional Scaling Rotation using External Variables', Neurocomputing, vol. 342, pp. 83-96, 2019.
• M. Mesnard, E. Payet, and W. Vanhoof, Towards a framework for algorithm recognition in binary code. In Principles and Practice of Declarative Programming, 2016. ACM Press.
• A. Narayan, E. Tuci, F. Labrosse, M.H.M. Alkilabi, A Dynamic Colour Perception System for Autonomous Robot Navigating on Unmarked Roads, Neurocomputing (Elsevier), Vol. 275, pp. 2251-2263, 2018.
• G. Perrouin, M. Acher, M. Cordy, X. Devroey. Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, MASES@ASE 2018, Montpellier, France, September 3, 2018. ACM 2018
• Y. Poullet. Le RGPD face à l'intelligence artificielle, Coll. du CRIDS n°49, Bruxelles, Larcier, 2020.
• A. Rouvroy and Y. Poullet. Le droit de la responsabilité des acteurs de l'intelligence artificielle, Colloquium organized by the UCLille, September 6 and 7, 2020.
• A. de Streel and H. Jacquemin (eds). L'intelligence artificielle et le droit, Coll. du CRIDS n°41, Bruxelles, Larcier, 2017.
• M. Vu and B. Frénay. User-steering Interpretable Visualization with Probabilistic Principal Components Analysis. in ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 349-354, 2019.

• ARIAC by DigitalWallonia4.ai (2021 - ...)
• BEM: Business Event Manager, Walloon project on workflow reconstructions (2010-13)
• EFFaTA-MEM: Evocative Framework for Text Analysis - MEdiality Models (2017 - ...)
• EOS VeriLearn : Verifying Learning Artificial Intelligence Systems (2017 - ...)
• DIGI4FED: Use of AI and Big Data to fight tax and social fraud (2020-2022)
• SEPLANS: Expert System in Estate Planning, Walloon project on estate planning with AI (2007-15).