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Francesca Mosca

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3 papers

2 author rows

JAAMAS Journal 2022 Journal Article

An explainable assistant for multiuser privacy

Francesca Mosca
Jose Such

Abstract Multiuser Privacy (MP) concerns the protection of personal information in situations where such information is co-owned by multiple users. MP is particularly problematic in collaborative platforms such as online social networks (OSN). In fact, too often OSN users experience privacy violations due to conflicts generated by other users sharing content that involves them without their permission. Previous studies show that in most cases MP conflicts could be avoided, and are mainly due to the difficulty for the uploader to select appropriate sharing policies. For this reason, we present ELVIRA, the first fully explainable personal assistant that collaborates with other ELVIRA agents to identify the optimal sharing policy for a collectively owned content. An extensive evaluation of this agent through software simulations and two user studies suggests that ELVIRA, thanks to its properties of being role-agnostic, adaptive, explainable and both utility- and value-driven, would be more successful at supporting MP than other approaches presented in the literature in terms of (i) trade-off between generated utility and promotion of moral values, and (ii) users’ satisfaction of the explained recommended output.

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ICAPS Conference 2022 Conference Paper

Explaining Preference-Driven Schedules: The EXPRES Framework

Alberto Pozanco
Francesca Mosca
Parisa Zehtabi
Daniele Magazzeni
Sarit Kraus

Scheduling is the task of assigning a set of scarce resources distributed over time to a set of agents, who typically have preferences over the assignments they would like to get. Due to the constrained nature of these problems, satisfying all agents' preferences often turns infeasible, which might lead to some agents not being happy with the resulting schedule. Providing explanations has been shown to increase satisfaction and trust in solutions produced by AI tools. However, explaining schedules poses some particular challenges such as problem interpretability (i. e. , generating explanations from a huge and dense amount of information) and privacy preservation (i. e. , generating explanations respecting the privacy of other agents involved). In this paper we introduce the EXPRES framework, that can explain why a given preference was unsatisfied in a given optimal schedule. The EXPRES framework consists of (i) an explanation generator, that, based on a Mixed-Integer Linear Programming model, finds the best set of reasons that can explain an unsatisfied preference; and (ii) an explanation parser, which translates the generated explanations into human interpretable ones, while preserving agents' privacy. Through simulations, we show that the explanation generator can efficiently scale to large instances. Finally, through a set of user studies within J. P. Morgan, we show that employees preferred the explanations generated by EXPRES over human-generated ones when considering workforce scheduling scenarios.

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AAMAS Conference 2021 Conference Paper

ELVIRA: An Explainable Agent for Value and Utility-Driven Multiuser Privacy

Francesca Mosca
Jose M. Such

Online social networks fail to support users to adequately share coowned content, which leads to privacy violations. Scholars proposed collaborative mechanisms to support users, but they did not satisfy one or more requirements needed according to empirical evidence in this domain, such as explainability, role-agnosticism, adaptability, and being utility- and value-driven. We present ELVIRA, an agent that supports multiuser privacy, whose design meets all these requirements. By considering the sharing preferences and the moral values of users, ELVIRA identifies the optimal sharing policy. Furthermore, ELVIRA justifies the optimality of the solution through explanations based on argumentation. We prove via simulations that ELVIRA provides solutions with the best trade-off between individual utility and value adherence. We also show through a user study that ELVIRA suggests solutions that are more acceptable than existing approaches and that its explanations are also more satisfactory.

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