Golnoosh Farnadi

prakhar.ganesh@mila.quebec

Marylou Fauchard Fauchard

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Margarida Carvalho

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 :

Margarida Carvalho

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

The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity

Prakhar Ganesh

Ihsan Ibrahim Daldaban

Ignacio Cofone

Model multiplicity, the phenomenon where multiple models achieve similar performance despite different underlying learned functions, introdu… (voir plus)ces arbitrariness in model selection. While this arbitrariness may seem inconsequential in expectation, its impact on individuals can be severe. This paper explores various individual concerns stemming from multiplicity, including the effects of arbitrariness beyond final predictions, disparate arbitrariness for individuals belonging to protected groups, and the challenges associated with the arbitrariness of a single algorithmic system creating a monopoly across various contexts. It provides both an empirical examination of these concerns and a comprehensive analysis from the legal standpoint, addressing how these issues are perceived in the anti-discrimination law in Canada. We conclude the discussion with technical challenges in the current landscape of model multiplicity to meet legal requirements and the legal gap between current law and the implications of arbitrariness in model selection, highlighting relevant future research directions for both disciplines.

2024-05-28

ArXiv (prépublication)

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… (voir plus)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.

2024-05-28

ArXiv (prépublication)

Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendations

Armin Moradi

Nicola Neophytou

2024-05-27

ArXiv (prépublication)

Fairness Incentives in Response to Unfair Dynamic Pricing

Jesse Thibodeau

Hadi Nekoei

Afaf Taïk

Janarthanan Rajendran

The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' … (voir plus)demand responses to a given pricing strategy. Notably, dynamic pricing may result in buyer distributions unreflective of those of the underlying population, which can be problematic in markets where fair representation is socially desirable. To address this, policy makers might leverage tools such as taxation and subsidy to adapt policy mechanisms dependent upon their social objective. In this paper, we explore the potential for AI methods to assist such intervention strategies. To this end, we design a basic simulated economy, wherein we introduce a dynamic social planner (SP) to generate corporate taxation schedules geared to incentivizing firms towards adopting fair pricing behaviours, and to use the collected tax budget to subsidize consumption among underrepresented groups. To cover a range of possible policy scenarios, we formulate our social planner's learning problem as a multi-armed bandit, a contextual bandit and finally as a full reinforcement learning (RL) problem, evaluating welfare outcomes from each case. To alleviate the difficulty in retaining meaningful tax rates that apply to less frequently occurring brackets, we introduce FairReplayBuffer, which ensures that our RL agent samples experiences uniformly across a discretized fairness space. We find that, upon deploying a learned tax and redistribution policy, social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings, and surpassing it by 13.19% in the full RL setting.

2024-04-22

ArXiv (prépublication)

Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract)

William St-Arnaud

Margarida Carvalho

2024-03-24

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Promoting Fair Vaccination Strategies through Influence Maximization: A Case Study on COVID-19 Spread

Nicola Neophytou

Afaf Taïk

The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such dispa… (voir plus)rities can be explained by several factors, including unequal access to healthcare, as well as the inability of low income groups to reduce their mobility due to work or social obligations. Moreover, senior citizens were found to be more susceptible to severe symptoms, largely due to age-related health reasons. Adapting vaccine distribution strategies to consider a range of demographics is therefore essential to address these disparities. In this study, we propose a novel approach that utilizes influence maximization (IM) on mobility networks to develop vaccination strategies which incorporate demographic fairness. By considering factors such as race, social status, age, and associated risk factors, we aim to optimize vaccine distribution to achieve various fairness definitions for one or more protected attributes at a time. Through extensive experiments conducted on Covid-19 spread in three major metropolitan areas across the United States, we demonstrate the effectiveness of our proposed approach in reducing disease transmission and promoting fairness in vaccination distribution.

2024-03-24

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards

Khaoula Chehbouni

Megha Roshan

Emmanuel Ma

Futian Andrew Wei

Afaf Taïk

Jackie Ck Cheung

2024-03-20

ArXiv (prépublication)

Balancing Act: Constraining Disparate Impact in Sparse Models

Meraj Hashemizadeh

Juan Ramirez

Rohan Sukumaran

Simon Lacoste-Julien

Jose Gallego-Posada

Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or … (voir plus)storage capacities. Although sparse models achieve performance comparable to that of their dense counterparts at the level of the entire dataset, they exhibit high accuracy drops for some data sub-groups. Existing methods to mitigate this disparate impact induced by pruning (i) rely on surrogate metrics that address the problem indirectly and have limited interpretability; or (ii) scale poorly with the number of protected sub-groups in terms of computational cost. We propose a constrained optimization approach that directly addresses the disparate impact of pruning: our formulation bounds the accuracy change between the dense and sparse models, for each sub-group. This choice of constraints provides an interpretable success criterion to determine if a pruned model achieves acceptable disparity levels. Experimental results demonstrate that our technique scales reliably to problems involving large models and hundreds of protected sub-groups.

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces

Ahmad-reza Ehyaei

Kiarash Mohammadi

Amir-Hossein Karimi

S. Samadi

2024-01-01

AAAI (publié)

Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery

Rebecca Salganik

Fernando Diaz

As online music platforms grow, music recommender systems play a vital role in helping users navigate and discover content within their vast… (voir plus) musical databases. At odds with this larger goal, is the presence of popularity bias, which causes algorithmic systems to favor mainstream content over, potentially more relevant, but niche items. In this work we explore the intrinsic relationship between music discovery and popularity bias. To mitigate this issue we propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems. Our approach uses individual fairness to reflect a ground truth listening experience, i.e., if two songs sound similar, this similarity should be reflected in their representations. In doing so, we facilitate meaningful music discovery that is robust to popularity bias and grounded in the music domain. We apply our BOOST methodology to two discovery based tasks, performing recommendations at both the playlist level and user level. Then, we ground our evaluation in the cold start setting, showing that our approach outperforms existing fairness benchmarks in both performance and recommendation of lesser-known content. Finally, our analysis explains why our proposed methodology is a novel and promising approach to mitigating popularity bias and improving the discovery of new and niche content in music recommender systems.

2024-01-01

ECIR (4) (publié)

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… (voir plus)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 (prépublication)

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 (publié)