11 citations · 27 across the 13 of their papers we have counts for
4 papers · 1 filter
Should I stay or should I show? Learning to selectively disclose information
Carlotta Giacchetta, Alessando Bogani, Cesare Barbera +4
In many high-stakes settings, human decision-makers can acquire support information before making a decision. However, acquiring information is costly, and disclosure may fail to i…
Personalized Causal Recourse: A Human-In-The-Loop Approach
Denise Tampieri, Giovanni De Toni, Paolo Giudici
Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios…
Divide et Calibra: Multiclass Local Calibration via Vector Quantization
Cesare Barbera, Lorenzo Perini, Giovanni De Toni +2
Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challenging: global approaches assume…
With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems
Giovanni De Toni, Cristian Consonni, Erasmo Purificato +2
Recommendation systems have become central gatekeepers of online information, shaping user behaviour across a wide range of activities. In response, users increasingly organize and…