Muhammad Bilal Zafar | DeepAI

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∙ 02/26/2023

Efficient fair PCA for fair representation learning

We revisit the problem of fair principal component analysis (PCA), where...

0 Matthäus Kleindessner, et al. ∙

∙ 12/23/2022

What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations

With over 500 million tweets posted per day, in Twitter, it is difficult...

0 Parantapa Bhattacharya, et al. ∙

∙ 03/21/2022

Diverse Counterfactual Explanations for Anomaly Detection in Time Series

Data-driven methods that detect anomalies in times series data are ubiqu...

0 Déborah Sulem, et al. ∙

∙ 12/23/2021

More Than Words: Towards Better Quality Interpretations of Text Classifiers

The large size and complex decision mechanisms of state-of-the-art text ...

0 Muhammad Bilal Zafar, et al. ∙

∙ 11/26/2021

Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models

With the increasing adoption of machine learning (ML) models and systems...

0 David Nigenda, et al. ∙

∙ 09/07/2021

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud

Understanding the predictions made by machine learning (ML) models and t...

30 Michaela Hardt, et al. ∙

∙ 07/13/2021

DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement

As the complexity of machine learning (ML) models increases, resulting i...

0 Umang Bhatt, et al. ∙

∙ 06/23/2021

Multi-objective Asynchronous Successive Halving

Hyperparameter optimization (HPO) is increasingly used to automatically ...

0 Robin Schmucker, et al. ∙

∙ 06/08/2021

On the Lack of Robust Interpretability of Neural Text Classifiers

With the ever-increasing complexity of neural language models, practitio...

0 Muhammad Bilal Zafar, et al. ∙

∙ 05/10/2021

Loss-Aversively Fair Classification

The use of algorithmic (learning-based) decision making in scenarios tha...

0 Junaid Ali, et al. ∙

∙ 05/07/2021

Pairwise Fairness for Ordinal Regression

We initiate the study of fairness for ordinal regression, or ordinal cla...

0 Matthäus Kleindessner, et al. ∙

∙ 07/01/2020

Unifying Model Explainability and Robustness via Machine-Checkable Concepts

As deep neural networks (DNNs) get adopted in an ever-increasing number ...

8 Vedant Nanda, et al. ∙

∙ 07/02/2018

A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices

Discrimination via algorithmic decision making has received considerable...

0 Till Speicher, et al. ∙

∙ 06/30/2017

From Parity to Preference-based Notions of Fairness in Classification

The adoption of automated, data-driven decision making in an ever expand...

0 Muhammad Bilal Zafar, et al. ∙

∙ 06/30/2017

On Fairness, Diversity and Randomness in Algorithmic Decision Making

Consider a binary decision making process where a single machine learnin...

0 Nina Grgić-Hlača, et al. ∙

∙ 10/31/2016

The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems

Bringing transparency to black-box decision making systems (DMS) has bee...

0 Miguel Ferreira, et al. ∙

∙ 10/26/2016

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being use...

0 Muhammad Bilal Zafar, et al. ∙