Ensuring fairness in AI is a complex challenge that requires bridging ethical principles with practical implementation. This talk explores key concepts in algorithmic fairness, from defining bias and fairness in predictive machine learning to assessing stigmas in large language models (LLMs). Using real-world examples, we illustrate the sociotechnical nature of fairness and the need for case-specific approaches.

As LLMs reshape the landscape, we examine new challenges, including how bias manifests differently across languages, methods for measuring these disparities, and strategies for mitigation. Finally, we advocate for an ethics-by-design mindset, emphasizing continuous monitoring, multidisciplinary collaboration, and proactive governance to ensure fairness remains at the core of AI development.

Load forecasting is essential for the efficient, reliable, and cost-effective management of power systems. Load forecasting performance can be improved by learning the similarities among multiple entities (e.g., regions, buildings). Techniques based on multi-task learning obtain predictions by leveraging consumption patterns from the historical load demand of multiple entities and their relationships. However, existing techniques cannot effectively assess inherent uncertainties in load demand or cannot account for dynamic changes in consumption patterns. This talk proposes a multi-task learning technique for online and probabilistic load forecasting. This technique provides accurate probabilistic predictions for loads of multiple entities by leveraging their dynamic similarities. The method's performance is evaluated using datasets that register the load demand of multiple entities and contain diverse and dynamic consumption patterns. The experimental results show that the proposed method can significantly enhance the effectiveness of current multi-task learning approaches across a wide variety of load consumption scenarios.

 In this talk we will look at the interplay between Shannon Classical Information Theory and Quantum Information.

The concepts of Classical Compression and Channel Coding will be related with their Quantum counterparts.

The use of artificial intelligence (AI) tools in all fields of human activity raises crucial questions about professional ethical responsibility. To what extent do AI techniques represent an additional ethical challenge compared to traditional computational tools? The talk focuses on the problem of explainability and understanding of work with AI by means of "black box" processes: how can an engineer, a doctor, a lawyer, assume ethical responsibility if he cannot rationally explain his work, both to himself and to others? A new understanding of ethics in the design and use of artificial intelligence technologies thus proves necessary.