Machine Learning

In recent years, safety and fairness have emerged as increasingly relevant topics in machine learning (ML), mainly because ML has also become an important and inseparable part of our daily lives. ML is everywhere: traffic prediction, recommendation systems, marketing analysis, medical diagnosis, autonomous driving, robot control, decision-making support for businesses and even governments make use of ML. ML systems have produced a disruptive change in society, enabling the automation of many tasks by leveraging the huge amount of information available in the Big Data era. For some applications, ML systems have shown impressive capabilities, even outperforming humans. Despite these achievements, the presence of ML in many real-world applications has brought new challenges related to the trustworthiness of these systems. The potential of these algorithms to cause undesirable behaviors is a growing concern in the ML community, especially when they are integrated in real-world safety-critical systems.