Search Results for author: Muhammad Bilal Zafar

Found 18 papers, 7 papers with code

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

no code implementations23 Dec 2022 Parantapa Bhattacharya, Saptarshi Ghosh, Muhammad Bilal Zafar, Soumya K. Ghosh, Niloy Ganguly

With over 500 million tweets posted per day, in Twitter, it is difficult for Twitter users to discover interesting content from the deluge of uninteresting posts.

Collaborative Filtering Recommendation Systems

More Than Words: Towards Better Quality Interpretations of Text Classifiers

no code implementations23 Dec 2021 Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi

The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of trust by the users.

Feature Importance

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

no code implementations26 Nov 2021 David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi

With the increasing adoption of machine learning (ML) models and systems in high-stakes settings across different industries, guaranteeing a model's performance after deployment has become crucial.

BIG-bench Machine Learning

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

1 code implementation13 Jul 2021 Umang Bhatt, Isabel Chien, Muhammad Bilal Zafar, Adrian Weller

In this work, we take a step towards finding influential training points that also represent the training data well.

Data Visualization Fairness

Multi-objective Asynchronous Successive Halving

2 code implementations23 Jun 2021 Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau

Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e. g., accuracy) of machine learning models.

Fairness Hyperparameter Optimization +3

On the Lack of Robust Interpretability of Neural Text Classifiers

no code implementations Findings (ACL) 2021 Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi

With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models.

Loss-Aversively Fair Classification

no code implementations10 May 2021 Junaid Ali, Muhammad Bilal Zafar, Adish Singla, Krishna P. Gummadi

Motivated by extensive literature in behavioral economics and behavioral psychology (prospect theory), we propose a notion of fair updates that we refer to as loss-averse updates.

Classification Decision Making +2

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.

Fair Bayesian Optimization

no code implementations9 Jun 2020 Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau

Moreover, our method can be used in synergy with such specialized fairness techniques to tune their hyperparameters.

Fairness

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

no code implementations2 Jul 2018 Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, Muhammad Bilal Zafar

Further, our work reveals overlooked tradeoffs between different fairness notions: using our proposed measures, the overall individual-level unfairness of an algorithm can be decomposed into a between-group and a within-group component.

Decision Making Fairness

On Fairness, Diversity and Randomness in Algorithmic Decision Making

no code implementations30 Jun 2017 Nina Grgić-Hlača, Muhammad Bilal Zafar, Krishna P. Gummadi, Adrian Weller

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans.

Decision Making Fairness

From Parity to Preference-based Notions of Fairness in Classification

1 code implementation NeurIPS 2017 Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi, Adrian Weller

The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups.

Classification Decision Making +2

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

no code implementations31 Oct 2016 Miguel Ferreira, Muhammad Bilal Zafar, Krishna P. Gummadi

Bringing transparency to black-box decision making systems (DMS) has been a topic of increasing research interest in recent years.

Decision Making Time Series

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

3 code implementations26 Oct 2016 Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi

To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates.

Decision Making Fairness +1

Fairness Constraints: Mechanisms for Fair Classification

2 code implementations19 Jul 2015 Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi

Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services.

Classification Decision Making +2

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