Portrait of Meghana Bhange

Meghana Bhange

meghana.bhange@mila.quebec
PhD - École de technologie suprérieure
Supervisor
Ulrich Aivodji
Co-supervisor
Samira Ebrahimi Kahou
Research Topics
Deep Learning
Information Theory
Natural Language Processing
Website
Google Scholar
GitHub

Publications

Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harms
Meghana Bhange
Ulrich Aivodji
Elliot Creager
When machine learning systems under-perform for particular subgroups, affected users typically have no way to correct these disparities with… (see more)out relying on platform-level fixes. Existing approaches to algorithmic fairness rely on provider-centric approaches to correct these failures, leaving users with no external lever when faced with harm. Recent work in Algorithmic Collective Action shows that coordinated users can steer an algorithmic system toward a collective goal, but the existing mechanisms require the provider to retrain on the collective's modified data which users may not have control over. We propose Test-Time Collective Action (TTCA), a framework through which a group of users who share query access to the platform, can correct disparities affecting under-served subgroup without participating in the platform's training loop. We implement this through a proxy-based mechanism where the collective pools query access to a black-box API to extract a proxy of the platform, then optimizes a per-class universal perturbation against the proxy. Each member applies this perturbation to their own inputs at submission time, requiring no cooperation from the platform. We empirically evaluate the mechanism on CIFAR-10, CIFAR-100, and FairFace, showing that modestly-sized collectives close most of the subgroup accuracy gap, transfer across architectures (a small proxy can attack a larger platform), and improve worst-group accuracy, equal-opportunity gap, and disparate impact. A query-budget analysis comparing a per-user black-box attack baseline shows that pooling is cheaper than each subgroup member attacking alone. Test-time collective action thus offers corrective intervention to users when platform-side remediation is unavailable or delayed.
2026-05-25
arXiv (preprint)
doi.org
arxiv.org
Survey on <scp>AI</scp> Ethics: A Socio‐Technical Perspective
Dave Mbiazi
Meghana Bhange
Maryam Babaei
Ivaxi Sheth
Patrik Joslin Kenfack
Samira Ebrahimi Kahou
Abstract The past decade has observed a significant advancement in AI, with deep learning‐based models being deployed in diverse scenarios… (see more), including safety‐critical applications. As these AI systems become deeply embedded in our societal infrastructure, the repercussions of their decisions and actions have significant consequences, making the ethical implications of AI deployment highly relevant and essential. The ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. These principles together form the foundations of ethical AI considerations that concern every stakeholder in the AI system lifecycle. In light of the present ethical and future x‐risk concerns, governments have shown increasing interest in establishing guidelines for the ethical deployment of AI. This work unifies the current and future ethical concerns of deploying AI into society. While we acknowledge and appreciate the technical surveys for each of the ethical principles concerned, in this paper, we aim to provide a comprehensive overview that not only addresses each principle from a technical point of view but also discusses them from a social perspective.
2025-11-01
Computational Intelligence (published)
doi.org
arxiv.org
Conscious Data Contribution via Community-Driven Chain-of-Thought Distillation
Lena Libon
Meghana Bhange
Rushabh Solanki
Elliot Creager
Ulrich Matchi Aïvodji
2025-09-22
NeurIPS.cc/2025/Workshop/ACA (oral)
openreview.net
openreview.net
Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy
Rushabh Solanki
Meghana Bhange
Ulrich Matchi Aïvodji
Elliot Creager
The integration of AI into daily life has generated considerable attention and excitement, while also raising concerns about automating algo… (see more)rithmic harms and re-entrenching existing social inequities. While top-down solutions such as regulatory policies and improved algorithm design are common, the fact that AI trains on social data creates an opportunity for a grassroots approach, Algorithmic Collective Action, where users deliberately modify the data they share to steer a platform's learning process in their favor. This paper considers how these efforts interact with a firm's use of a differentially private model to protect user data, motivated by the growing regulatory focus on privacy and data protection. In particular, we investigate how the use of Differentially Private Stochastic Gradient Descent (DPSGD) affects the collective’s ability to influence the learning process. Our findings show that while differential privacy contributes to the protection of individual data, it introduces challenges for effective algorithmic collective action. We characterize lower bounds on the success of these actions as a function of the collective's size and the firm's privacy parameters, verifying these trends experimentally by training deep neural network classifiers across several datasets.
2025-09-22
NeurIPS.cc/2025/Workshop/ACA (poster)
openreview.net
openreview.net