Search Results for author: Vedant Nanda

Found 7 papers, 3 papers with code

Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual

no code implementations16 Jan 2022 Seyed A. Esmaeili, Sharmila Duppala, Vedant Nanda, Aravind Srinivasan, John P. Dickerson

In the most general form, the platform consists of three entities: two sides to be matched and a platform operator that decides the matching.


Exploring Alignment of Representations with Human Perception

no code implementations29 Nov 2021 Vedant Nanda, Ayan Majumdar, Camila Kolling, John P. Dickerson, Krishna P. Gummadi, Bradley C. Love, Adrian Weller

We argue that a valuable perspective on when a model learns \textit{good} representations is that inputs that are mapped to similar representations by the model should be perceived similarly by humans.

Data Augmentation Self-Supervised Learning

Technical Challenges for Training Fair Neural Networks

no code implementations12 Feb 2021 Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P. Dickerson, Tom Goldstein

As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes settings (such as facial recognition and medical imaging).

Fairness Medical Diagnosis

Unifying Model Explainability and Robustness via Machine-Checkable Concepts

no code implementations1 Jul 2020 Vedant Nanda, Till Speicher, John P. Dickerson, Krishna P. Gummadi, Muhammad Bilal Zafar

Our framework defines a large number of concepts that the DNN explanations could be based on and performs the explanation-conformity check at test time to assess prediction robustness.

Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning

1 code implementation17 Jun 2020 Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson

In this paper, we argue that traditional notions of fairness that are only based on models' outputs are not sufficient when the model is vulnerable to adversarial attacks.

Decision Making Face Recognition +1

Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours

1 code implementation18 Dec 2019 Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman, John P. Dickerson, Aravind Srinivasan

Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e. g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver.


On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning

1 code implementation4 Mar 2019 Hoda Heidari, Vedant Nanda, Krishna P. Gummadi

Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population.

Decision Making Fairness

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