26 citations · 76 across the 36 of their papers we have counts for
16 papers · 1 filter
Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML
Prakhar Ganesh, Usman Gohar, Lu Cheng +1
With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often compared against each other to find the…
LoRA Provides Differential Privacy by Design via Random Sketching
Saber Malekmohammadi, Golnoosh Farnadi
Low-rank adaptation of language models has been proposed to reduce the computational and memory overhead of fine-tuning pre-trained language models. LoRA incorporates trainable low…
What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models
Ahmed Imtiaz Humayun, Ibtihel Amara, Cristina Vasconcelos +7
Deep Generative Models are frequently used to learn continuous representations of complex data distributions using a finite number of samples. For any generative model, including p…
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
The rise of foundation models holds immense promise for advancing AI, but this progress may amplify existing risks and inequalities, leaving marginalized communities behind. In thi…
Differentially Private Clustered Federated Learning
Saber Malekmohammadi, Afaf Taik, Golnoosh Farnadi
Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide rigorous data privacy guarantees. Previous…
Fairness Incentives in Response to Unfair Dynamic Pricing
Jesse Thibodeau, Hadi Nekoei, Afaf Taïk +2
The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' demand responses to a given pricing stra…
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Khaoula Chehbouni, Megha Roshan, Emmanuel Ma +4
Recent progress in large language models (LLMs) has led to their widespread adoption in various domains. However, these advancements have also introduced additional safety risks an…
Balancing Act: Constraining Disparate Impact in Sparse Models
Meraj Hashemizadeh, Juan Ramirez, Rohan Sukumaran +3
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or storage capacities. Although sparse mode…
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi
Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often studied in isolation. Adversarial perturba…
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
Ahmad-Reza Ehyaei, Kiarash Mohammadi, Amir-Hossein Karimi +2
As responsible AI gains importance in machine learning algorithms, properties such as fairness, adversarial robustness, and causality have received considerable attention in recent…
Analyzing the Effect of Sampling in GNNs on Individual Fairness
Rebecca Salganik, Fernando Diaz, Golnoosh Farnadi
Graph neural network (GNN) based methods have saturated the field of recommender systems. The gains of these systems have been significant, showing the advantages of interpreting d…
FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks
Kiarash Mohammadi, Aishwarya Sivaraman, Golnoosh Farnadi
Algorithmic decision making driven by neural networks has become very prominent in applications that directly affect people's quality of life. In this paper, we study the problem o…
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning
Sikha Pentyala, Nicola Neophytou, Anderson Nascimento +2
Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute suc…
PrivFair: a Library for Privacy-Preserving Fairness Auditing
Sikha Pentyala, David Melanson, Martine De Cock +1
Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance. ML models have been found t…
Counterexample-Guided Learning of Monotonic Neural Networks
Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein +1
The widespread adoption of deep learning is often attributed to its automatic feature construction with minimal inductive bias. However, in many real-world tasks, the learned funct…
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
YooJung Choi, Golnoosh Farnadi, Behrouz Babaki +1
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making. Existing methods of…