Golnoosh Farnadi

Alireza Dehghanpour Farashah

Master's Research - McGill University

Co-supervisor :

Master's Research - Université de Montréal

Principal supervisor :

Margarida Carvalho

marylou.fauchard@mila.quebec

Prakhar Ganesh

PhD - McGill University

prakhar.ganesh@mila.quebec

Website

Google Scholar

Aly Kassem

Collaborating researcher - UWindsor

PhD - McGill University

Co-supervisor :

Master's Research - Université de Montréal

Nahian Rahman

Research Intern - McGill University

Elaheh Rahmati

Master's Research - Polytechnique Montréal

Zhuan Shi

Postdoctorate - McGill University

William St-Arnaud

PhD - Université de Montréal

Co-supervisor :

Margarida Carvalho

Rohan Sukumaran

Master's Research - Université de Montréal

Website

Google Scholar

Afaf Taïk

Postdoctorate - Université de Montréal

Jesse Thibodeau

Independent visiting researcher - HEC Montréal

Publications

Understanding Intrinsic Socioeconomic Biases in Large Language Models

Mina Arzaghi

Florian Carichon

Large Language Models (LLMs) are increasingly integrated into critical decision-making processes, such as loan approvals and visa applicatio… (see more)ns, where inherent biases can lead to discriminatory outcomes. In this paper, we examine the nuanced relationship between demographic attributes and socioeconomic biases in LLMs, a crucial yet understudied area of fairness in LLMs. We introduce a novel dataset of one million English sentences to systematically quantify socioeconomic biases across various demographic groups. Our findings reveal pervasive socioeconomic biases in both established models such as GPT-2 and state-of-the-art models like Llama 2 and Falcon. We demonstrate that these biases are significantly amplified when considering intersectionality, with LLMs exhibiting a remarkable capacity to extract multiple demographic attributes from names and then correlate them with specific socioeconomic biases. This research highlights the urgent necessity for proactive and robust bias mitigation techniques to safeguard against discriminatory outcomes when deploying these powerful models in critical real-world applications.

2024-01-01

AIES (1) (published)

Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness

Ahmad-reza Ehyaei

Samira Samadi

Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often st… (see more)udied in isolation. Adversarial perturbation, used to identify vulnerabilities in models, and individual fairness, aiming for equitable treatment of similar individuals, despite initial differences, both depend on metrics to generate comparable input data instances. Previous attempts to define such joint metrics often lack general assumptions about data or structural causal models and were unable to reflect counterfactual proximity. To address this, our paper introduces a causal fair metric formulated based on causal structures encompassing sensitive attributes and protected causal perturbation. To enhance the practicality of our metric, we propose metric learning as a method for metric estimation and deployment in real-world problems in the absence of structural causal models. We also demonstrate the application of our novel metric in classifiers. Empirical evaluation of real-world and synthetic datasets illustrates the effectiveness of our proposed metric in achieving an accurate classifier with fairness, resilience to adversarial perturbations, and a nuanced understanding of causal relationships.

2023-10-30

ArXiv (preprint)

FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks

Kiarash Mohammadi

Aishwarya Sivaraman

2023-10-30

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

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 (published)

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… (see more) 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 (preprint)

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… (see more)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 (published)

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 … (see more)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 (preprint)

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… (see more) 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

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 (published)

Individual Fairness in Kidney Exchange Programs

William St-Arnaud

Behrouz Babaki

Margarida Carvalho

2021-05-18

AAAI Conference on Artificial Intelligence (published)

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… (see more)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 (published)