Manuel Gomez-Rodriguez awarded ERC Starting Grant
Manuel Gomez-Rodriguez, head of the MPI-SWS Human-Centric Machine Learning group, has been awarded an ERC Starting Grant. Over the next five years, his project „Human-Centric Machine Learning“ will receive 1.49 million euros, which will allow the group to develop the foundations of human-centric machine learning.
In the most recent round for Starting Grants, over 3300 research proposals were submitted to the ERC. The sole selection criterion is scientific excellence. This year, less than 14% of all ERC Starting Grant applicants across all scientific disciplines received the award, with only 20 awardees in Computer Science across all of Europe!
Summary of the HumanML project proposal
With the advent of mass-scale digitization of information and virtually limitless computational power, an increasing number of social, information and cyber-physical systems evaluate, support or even replace human decisions using machine learning models and algorithms. Machine learning models and algorithms have been traditionally designed to take decisions autonomously, without human intervention, on the basis of passively collected data. However, in most social, information and cyber-physical systems, algorithmic and human decisions feed on and influence each other. As these decisions become more consequential to individuals and society, machine learning models and algorithms have been blamed for playing a major role in an increasing number of missteps, from discriminating against minorities, causing car accidents and increasing polarization to misleading people in social media.
In this project, we will develop human-centric machine learning models and algorithms for evaluating, supporting and enhancing decision-making processes where algorithmic and human decisions feed on and influence each other. These models and algorithms will account for the feedback loop between algorithmic and human decisions, which currently perpetuates or even amplifies biases and inequalities, and they will learn to operate under different automation levels. Moreover, they will anticipate how individuals will react to their algorithmic decisions, often strategically, to receive beneficial decisions and they will provide actionable insights about their algorithmic decisions. Finally, we will perform observational and interventional experiments as well as realistic simulations to evaluate their effectiveness in a wide range of applications, from content moderation, recidivism prediction, and credit scoring to medical diagnosis and autonomous driving.