Dynamic value alignment through preference aggregation of multiple objectives

As artificial intelligence (AI) research reaches new peaks, more and more AI systems are being implemented, applied, and deployed worldwide. Further integration of such systems with human societies demands a thorough consideration of their consequences and effects. The inherent property of most, if not all, AI systems is to act with an unprecedented level of autonomy, often in settings where its actions might directly affect human beings. The growing field of Value Alignment (VA) aims to explicitly study the values pursued and exhibited by AI agents and make sure that they correspond to human values. Motivating examples of VA often consider the long-term and potentially existential threats posed by powerful, superintelligent AI agents with misaligned values [Russell, 2022a]. Not less pertinent are the short-term threats of more mundane, highly specialized AI systems, employed in particular in control settings, becoming misaligned. A prominent case where a potential misalignment is particularly dangerous is given by systems where humans voluntarily cede control of a system to algorithms. Examples of such systems abound: self-driving cars, where the driver cedes control of their vehicle [Haboucha et al., 2017]; recommender systems and content algorithms [Carissimo et al., 2023], where the user cedes some control over their access to information; traffic control systems, where drivers cede control of traffic flow coordination [Korecki and Helbing, 2022], are all examples of systems where AI is a control method of choice or is in the process of becoming one