Search Results for author: Vikram V. Ramaswamy

Found 7 papers, 4 papers with code

Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability

1 code implementation20 Jul 2022 Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky

Second, we find that concepts in the probe dataset are often less salient and harder to learn than the classes they claim to explain, calling into question the correctness of the explanations.

Gender Artifacts in Visual Datasets

no code implementations18 Jun 2022 Nicole Meister, Dora Zhao, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky

Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models.

ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features

no code implementations15 Jun 2022 Vikram V. Ramaswamy, Sunnie S. Y. Kim, Nicole Meister, Ruth Fong, Olga Russakovsky

Specifically, we develop a novel explanation framework ELUDE (Explanation via Labelled and Unlabelled DEcomposition) that decomposes a model's prediction into two parts: one that is explainable through a linear combination of the semantic attributes, and another that is dependent on the set of uninterpretable features.

Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation

1 code implementation10 May 2022 Angelina Wang, Vikram V. Ramaswamy, Olga Russakovsky

In this work, we grapple with questions that arise along three stages of the machine learning pipeline when incorporating intersectionality as multiple demographic attributes: (1) which demographic attributes to include as dataset labels, (2) how to handle the progressively smaller size of subgroups during model training, and (3) how to move beyond existing evaluation metrics when benchmarking model fairness for more subgroups.

BIG-bench Machine Learning Fairness

HIVE: Evaluating the Human Interpretability of Visual Explanations

1 code implementation6 Dec 2021 Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky

As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable.

Decision Making

Fair Attribute Classification through Latent Space De-biasing

1 code implementation CVPR 2021 Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky

Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world.

Classification Fairness +1

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