With the advent of mass-scale digitization of information and virtually limitless computational power, an in- creasing 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 to play a major role in an increasing number of missteps, from discriminating minorities, causing car accidents and increasing polarization to misleading people in social media.
In this project, we are developing 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 account for the feedback loop between algorithmic and human decisions, which currently perpetuates or even amplifies biases and inequalities, and they learn to operate under different automation levels. Moreover, they 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 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.
Improving Expert Predictions with Conformal Prediction, ICML 2023.
On the Within-Group Discrimination of Screening Classifiers, ICML 2023.
Counterfactual Temporal Point Processes, NeurIPS 2022.
Counterfactual Inference of Second Opinions, UAI 2022.
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes, TMLR 2022.
Improving Screening Processes via Calibrated Subset Selection, ICML 2022.
Bayesian Persuasion in Sequential Decision-Making, AAAI 2022.
Admissible Policy Teaching through Reward Design, AAAI 2022.
Defense coordination in security games: equilibrium analysis and mechanism design, Artificial Intelligence 2022.
Pooled Testing of Traced Contacts Under Superspreading Dynamics, PLOS Computational Biology, 2022.
Counterfactual Explanations in Sequential Decision Making Under Uncertainty, NeurIPS 2021.
Differentiable Learning under Triage, NeurIPS 2021.
Large-scale randomized experiments reveals that machine learning-based instruction helps people memorize more effectively, npj Science of Learning, 2021.
Classification Under Human Assistance, AAAI 2021.