Using the example of the film 2001: A Space Odyssey, this chapter illustrates the challenges posed by an AI capable of making decisions that go against human interests. But are human decisions always rational and ethical? In reality, the cognitive decision-making process is influenced by cognitive biases that affect our behavior and choices. AI not only reproduces these biases, but can also exploit them, with the potential to shape our decisions and judgments. Behind IA algorithms, there are sometimes individuals who show little concern for fundamental rights and impose their own rules. To address the ethical and societal challenges raised by AI and its governance, the regulation of digital platforms and education are keys levers. Regulation must reflect ethical, legal, and political choices, while education must strengthen digital literacy and teach people to make informed and critical choices when facing digital technologies.

Multi-agent simulations enables the modeling and analyses of the dynamic behaviors and interactions of autonomous entities evolving in complex environments. Agent-based models (ABM) are widely used to study emergent phenomena arising from local interactions. However, their high computational cost poses a significant challenge, particularly for large-scale simulations requiring extensive parameter exploration, optimization, or uncertainty quantification. The increasing complexity of ABM limits their feasibility for real-time decision-making and large-scale scenario analysis. To address these limitations, surrogate models offer an efficient alternative by learning approximations from sparse simulation data. These models provide cheap-to-evaluate predictions, significantly reducing computational costs while maintaining accuracy. Various machine learning techniques, including regression models, neural networks, random forests and Gaussian processes, have been applied to construct robust surrogates. Moreover, uncertainty quantification and sensitivity analysis play a crucial role in enhancing model reliability and interpretability. This article explores the motivations, methods, and applications of surrogate modeling for ABM, emphasizing the trade-offs between accuracy, computational efficiency, and interpretability. Through a case study on a segregation model, we highlight the challenges associated with building and validating surrogate models, comparing different approaches and evaluating their performance. Finally, we discuss future perspectives on integrating surrogate models within ABM to improve scalability, explainability, and real-time decision support across various fields such as ecology, urban planning and economics.

Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing maximum likelihood estimate when dealing with Gaussian Mixture Model (GMM). When the sample size is smaller than the data dimension, this could lead to a singular or poorly conditioned covariance matrix and, thus, to performance reduction. This paper presents a regularized version of the EM algorithm that efficiently uses prior knowledge to cope with a small sample size. This method aims to maximize a penalized GMM likelihood where regularized estimation may ensure positive definiteness of covariance matrix updates by shrinking the estimators towards some structured target covariance matrices. Finally, experiments on real data highlight the good performance of the proposed algorithm for clustering purposes.

Logs record valuable system information at runtime. They are widely used by data-driven approaches for development and monitoring purposes. Parsing log messages to structure their format is a classic preliminary step for log-mining tasks. As they appear upstream, parsing operations can become a processing time bottleneck for downstream applications. The quality of parsing also has a direct influence on their efficiency. Here, we propose USTEP, an online log parsing method based on an evolving tree structure. Evaluation results on a wide panel of datasets coming from different real-world systems demonstrate USTEP superiority in terms of both effectiveness and robustness when compared to other online methods.

The introduction of the Torin Artificial Intelligence (AI) functionality is designed to improve efficiency in managing schedules for surgical procedures and comes in the wake of a new Getinge survey. Among the participating hospitals 41% report significant backlogs and only 44% have implemented new digital tools that can improve proficiency in OR scheduling and patient management. Getinge, a leading global provider of products and solutions that contribute to quality enhancement and cost efficiency within healthcare and life sciences, now introduces Torin with AI in the United States. The company also announced results from a landmark survey of hospital executives and surgeons in the U.S. showing hospitals are taking steps to speed up OR turnover times, hire new staff and require staff to work longer hours to address backlogs. "For almost 18 months during the COVID-19 pandemic, both surgeons and patients made decisions to defer many forms of surgery if possible. As more patients feel confident about considering surgery, demand to schedule procedures at all types of hospitals and surgery centers has exploded in recent months," says Eric Honroth, President, North America at Getinge.

Crowdsourcing is defined as the outsourcing of tasks to a crowd of contributors. The crowd is very diverse on these platforms and includes malicious contributors attracted by the remuneration of tasks and not conscientiously performing them. It is essential to identify these contributors in order to avoid considering their responses. As not all contributors have the same aptitude for a task, it seems appropriate to give weight to their answers according to their qualifications. This paper, published at the ICTAI 2019 conference, proposes a method, MONITOR, for estimating the profile of the contributor and aggregating the responses using belief function theory.

In the latest version of Ortoma Treatment Solution, OTS 4, Ortoma has introduced support of Artificial Intelligence. The system drastically reduces the time needed for pre-operatively planning of a hip implant in 3D, which has created a big interest for the company's unique solution. Performance has been a focus area during the development of the latest version of Ortoma Treatment Solution (OTS). With OTS 4, an automatic AI-analysis, which forms the basis for the pre-operative planning, is made. The analysis does not take more than about 30 seconds and includes a suggestion for suitable implant and its optimized position.

During 15-17 September, RaySearch will exhibit its latest advances in oncology software at the American Society for Radiation Oncology (ASTRO) 2019 annual meeting in Chicago, USA. On show will be new development in machine learning technology and automation in the RayStation* treatment planning system and the RayCare * oncology information system. Machine learning capabilities were added already in RayStation 8B and are being continuously improved. RaySearch has now been granted FDA 510(k) clearance for deep learning organ segmentation and for machine learning automated planning for a key model. Several planning models are being validated for future FDA 510(k) clearance.

During 15-17 September, RaySearch will exhibit its latest advances in oncology software at the American Society for Radiation Oncology (ASTRO) 2019 annual meeting in Chicago, USA. On show will be new development in machine learning technology and automation in the RayStation* treatment planning system and the RayCare * oncology information system. Machine learning capabilities were added already in RayStation 8B and are being continuously improved. RaySearch has now been granted FDA 510(k) clearance for deep learning organ segmentation and for machine learning automated planning for a key model. Several planning models are being validated for future FDA 510(k) clearance.

Task 2 is a task on semantic similarity between clinical cases and discussions. For this task, we propose an approach based on language models and evaluate the impact on the results of different preprocessings and matching techniques. For task 3, we have developed an information extraction system yielding very encouraging results accuracy-wise. We have experimented two different approaches, one based on the exclusive use of neural networks, the other based on a linguistic analysis.