A researcher in philosophy of technology, Alix Rübsaam investigates the societal and cultural impact of exponential technologies. She focuses on human activity in technological contexts such as Artificial Intelligence, information technologies, and digital environments and on deploying emerging technologies responsibly.
Alix’ research centres around two projects. The first explores responsible AI, algorithmic bias and exclusion. The aim is to map the impact of automated decision-making algorithms, to explicate how design decisions can lead to unwanted outcomes, and to empower leaders to build AI that is equitable, fair, and just. The second project investigates the digitalisation of information, and its effects on decision making. The goal is to investigate and challenge computational paradigms and to reposition leaders to informed decision making in a manner that befits 21st century dilemmas.
Alix is VP of Research, Expertise and Knowledge at Singularity. She oversees SU’s research efforts, body of knowledge, and community of experts. Prior to this, Alix was a PhD candidate at the University of Amsterdam and ASCA. She researched the collaboration between (technological) agents at the intersection of humans and computational systems. She has written and speaks about cyberpunk and science fiction literature, autonomous weapon systems, embodied robotics, and (responsible) AI.
This immersive simulation goes through the steps of designing and training an AI. Participants learn how decisions made in the design phase influence the outcomes of the algorithms they build. They learn how to identify and analyze the mechanisms that lead to unintended outcomes in AI, and how to advocate for and build more socially responsible algorithms. During the workshop, participants will make decisions about the training and designing of algorithms in a simulation of real world implementation of technologies across industries. Shortening the distance between design, implementation, and outcomes, increases understanding of how cultural backgrounds and assumptions become programmed into data-driven technologies. Then, participants will learn to identify opportunities for responsible AI; pinpoint potential pitfalls for algorithmic bias; and learn to assess the risks for unintended outcomes. During the simulation, they will also become familiar with the design, implementation, and use of automated decision-making algorithms and Machine Learning systems.