Golnoosh Farnadi

Marylou Fauchard Fauchard

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

prakhar.ganesh@mila.quebec

maryfau@icloud.com

Prakhar Ganesh

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Stagiaire de recherche

saber.malekmohammadi@mila.quebec

Armin Moradi

Maîtrise recherche - UdeM

arminmoradi785@gmail.com

Maîtrise recherche - UdeM

Nicola Neophytou

Collaborateur·rice alumni - UdeM

Google Scholar

Elaheh Rahmati

Maîtrise recherche - Polytechnique

Reinhardt Sebastian

Stagiaire de recherche - McGill University

William St-Arnaud

Doctorat - UdeM

Co-superviseur⋅e :

Rohan Sukumaran

Maîtrise recherche - UdeM

Site web

Google Scholar

Afaf Taïk

Postdoctorat - UdeM

Jesse Thibodeau

Visiteur de recherche indépendant - HEC

Milan Van den Heuvel

Visiteur de recherche indépendant - Ghent University

Publications

Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements

Maryam Molamohammadi

Afaf Taïk

Nicolas Le Roux

2023-10-30

Equity and Access in Algorithms, Mechanisms, and Optimization (publié)

Tidying Up the Conversational Recommender Systems' Biases

Armin Moradi

The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research… (voir plus) circles. However, concerns regarding biases in these systems have emerged. While individual components of CRS have been subject to bias studies, a literature gap remains in understanding specific biases unique to CRS and how these biases may be amplified or reduced when integrated into complex CRS models. In this paper, we provide a concise review of biases in CRS by surveying recent literature. We examine the presence of biases throughout the system's pipeline and consider the challenges that arise from combining multiple models. Our study investigates biases in classic recommender systems and their relevance to CRS. Moreover, we address specific biases in CRS, considering variations with and without natural language understanding capabilities, along with biases related to dialogue systems and language models. Through our findings, we highlight the necessity of adopting a holistic perspective when dealing with biases in complex CRS models.

2023-09-05

ArXiv (prépublication)

Social Media as a Vector for Escort Ads:A Study on OnlyFans advertisements on Twitter

Maricarmen Arenas

Pratheeksha Nair

Reihaneh Rabbany

Online sex trafficking is on the rise and a majority of trafficking victims report being advertised online. The use of OnlyFans as a platfor… (voir plus)m for adult content is also increasing, with Twitter as its main advertising tool. Furthermore, we know that traffickers usually work within a network and control multiple victims. Consequently, we suspect that there may be networks of traffickers promoting multiple OnlyFans accounts belonging to their victims. To this end, we present the first study of OnlyFans advertisements on Twitter in the context of finding organized activities. Preliminary analysis of this space shows that most tweets related to OnlyFans contain generic text, making text-based methods less reliable. Instead, focusing on what ties the authors of these tweets together, we propose a novel method for uncovering coordinated networks of users based on their behaviour. Our method, called Multi-Level Clustering (MLC), combines two levels of clustering that considers both the network structure as well as embedded node attribute information. It focuses jointly on user connections (through mentions) and content (through shared URLs). We apply MLC to real-world data of 2 million tweets pertaining to OnlyFans and analyse the detected groups. We also evaluate our method on synthetically generated data (with injected ground truth) and show its superior performance compared to competitive baselines. Finally, we discuss examples of organized clusters as case studies and provide interesting conclusions to our study.

2023-04-30

Proceedings of the 15th ACM Web Science Conference 2023 (publié)

Privacy-Preserving Fair Item Ranking

Jiajun Sun

Sikha Pentyala

Martine De Cock

Users worldwide access massive amounts of curated data in the form of rankings on a daily basis. The societal impact of this ease of access … (voir plus)has been studied and work has been done to propose and enforce various notions of fairness in rankings. Current computational methods for fair item ranking rely on disclosing user data to a centralized server, which gives rise to privacy concerns for the users. This work is the first to advance research at the conjunction of producer (item) fairness and consumer (user) privacy in rankings by exploring the incorporation of privacy-preserving techniques; specifically, differential privacy and secure multi-party computation. Our work extends the equity of amortized attention ranking mechanism to be privacy-preserving, and we evaluate its effects with respect to privacy, fairness, and ranking quality. Our results using real-world datasets show that we are able to effectively preserve the privacy of users and mitigate unfairness of items without making additional sacrifices to the quality of rankings in comparison to the ranking mechanism in the clear.

2023-03-06

ArXiv (prépublication)

Early Detection of Sexual Predators with Federated Learning

Khaoula Chehbouni

Gilles Caporossi

Reihaneh Rabbany

Martine De Cock

The rise in screen time and the isolation brought by the different containment measures implemented during the COVID-19 pandemic have led to… (voir plus) an alarming increase in cases of online grooming. Online grooming is defined as all the strategies used by predators to lure children into sexual exploitation. Previous attempts made in industry and academia on the detection of grooming rely on accessing and monitoring users’ private conversations through the training of a model centrally or by sending personal conversations to a global server. We introduce a first, privacy-preserving, cross-device, federated learning framework for the early detection of sexual predators, which aims to ensure a safe online environment for children while respecting their privacy.

2022-10-20

NeurIPS.cc/2022/Workshop/Federated_Learning (poster)

openreview.net

Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs

William St-Arnaud

In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (voir plus)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.

2022-07-01

ArXiv (prépublication)

A taxonomy of weight learning methods for statistical relational learning

Sriram Srinivasan

Charles Dickens

Eriq Augustine

Lise Getoor

2021-12-13

Machine-mediated learning (published)

A taxonomy of weight learning methods for statistical relational learning

Sriram Srinivasan

Charles Dickens

Eriq Augustine

Lise Getoor

2021-12-13

Machine-mediated learning (publié)

Individual Fairness in Kidney Exchange Programs

William St-Arnaud

Behrouz Babaki

2021-05-18

AAAI Conference on Artificial Intelligence (publié)

A Unifying Framework for Fairness-Aware Influence Maximization

Behrouz Babaki

Michel Gendreau

The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studi… (voir plus)ed over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms.

2020-04-20

Companion Proceedings of the Web Conference 2020 (publié)

Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration

Behrouz Babaki