11 citations · 27 across the 13 of their papers we have counts for
14 papers
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…
To Ask or Not to Ask: Learning to Require Human Feedback
Andrea Pugnana, Giovanni De Toni, Cesare Barbera +3
Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machi…
Multiclass Local Calibration with the Jensen-Shannon Distance
Cesare Barbera, Lorenzo Perini, Giovanni De Toni +2
Developing trustworthy Machine Learning (ML) models requires their predicted probabilities to be well-calibrated, meaning they should reflect true-class frequencies. Among calibrat…
Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden
Ainhize Barrainkua, Giovanni De Toni, Jose Antonio Lozano +1
Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring…
You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control
Giovanni De Toni, Erasmo Purificato, Emilia Gómez +3
Recommenders are significantly shaping online information consumption. While effective at personalizing content, these systems increasingly face criticism for propagating irrelevan…
Time Can Invalidate Algorithmic Recourse
Giovanni De Toni, Stefano Teso, Bruno Lepri +1
Algorithmic Recourse (AR) aims to provide users with actionable steps to overturn unfavourable decisions made by machine learning predictors. However, these actions often take time…
Towards Human-AI Complementarity with Prediction Sets
Giovanni De Toni, Nastaran Okati, Suhas Thejaswi +2
Decision support systems based on prediction sets have proven to be effective at helping human experts solve classification tasks. Rather than providing single-label predictions, t…
Personalized Algorithmic Recourse with Preference Elicitation
Giovanni De Toni, Paolo Viappiani, Stefano Teso +2
Algorithmic Recourse (AR) is the problem of computing a sequence of actions that -- once performed by a user -- overturns an undesirable machine decision. It is paramount that the…
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
Giovanni De Toni, Bruno Lepri, Andrea Passerini
Being able to provide counterfactual interventions - sequences of actions we would have had to take for a desirable outcome to happen - is essential to explain how to change an unf…
Learning compositional programs with arguments and sampling
Giovanni De Toni, Luca Erculiani, Andrea Passerini
One of the most challenging goals in designing intelligent systems is empowering them with the ability to synthesize programs from data. Namely, given specific requirements in the…
A general method for estimating the prevalence of Influenza-Like-Symptoms with Wikipedia data
Giovanni De Toni, Cristian Consonni, Alberto Montresor
Influenza is an acute respiratory seasonal disease that affects millions of people worldwide and causes thousands of deaths in Europe alone. Being able to estimate in a fast and re…