626 citations · 1.5k across the 21 of their papers we have counts for
23 papers
Rote Learning Considered Useful: Generalizing over Memorized Data in LLMs
Qinyuan Wu, Soumi Das, Mahsa Amani +4
Rote learning is a memorization technique based on repetition. Many researchers argue that rote learning hinders generalization because it encourages verbatim memorization rather t…
Efficient fair PCA for fair representation learning
Matthäus Kleindessner, Michele Donini, Chris Russell +1
We revisit the problem of fair principal component analysis (PCA), where the goal is to learn the best low-rank linear approximation of the data that obfuscates demographic informa…
What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations
Parantapa Bhattacharya, Saptarshi Ghosh, Muhammad Bilal Zafar +2
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. In this work, we…
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Deborah Sulem, Michele Donini, Muhammad Bilal Zafar +6
Data-driven methods that detect anomalies in times series data are ubiquitous in practice, but they are in general unable to provide helpful explanations for the predictions they m…
More Than Words: Towards Better Quality Interpretations of Text Classifiers
Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini +4
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 tru…
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models
David Nigenda, Zohar Karnin, Muhammad Bilal Zafar +4
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 ha…
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng +18
Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the da…
DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement
Umang Bhatt, Isabel Chien, Muhammad Bilal Zafar +1
As the complexity of machine learning (ML) models increases, resulting in a lack of prediction explainability, several methods have been developed to explain a model's behavior in…
Multi-objective Asynchronous Successive Halving
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar +2
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e.g., accuracy) of machine learning models. However, in a plethora of real-…
On the Lack of Robust Interpretability of Neural Text Classifiers
Muhammad Bilal Zafar, Michele Donini, Dylan Slack +3
With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models. One of the most well-adopted…
Loss-Aversively Fair Classification
Junaid Ali, Muhammad Bilal Zafar, Adish Singla +1
The use of algorithmic (learning-based) decision making in scenarios that affect human lives has motivated a number of recent studies to investigate such decision making systems fo…
Accounting for Model Uncertainty in Algorithmic Discrimination
Junaid Ali, Preethi Lahoti, Krishna P. Gummadi
Traditional approaches to ensure group fairness in algorithmic decision making aim to equalize ``total'' error rates for different subgroups in the population. In contrast, we argu…
Pairwise Fairness for Ordinal Regression
Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar +2
We initiate the study of fairness for ordinal regression. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that i…
Unifying Model Explainability and Robustness via Machine-Checkable Concepts
Vedant Nanda, Till Speicher, John P. Dickerson +2
As deep neural networks (DNNs) get adopted in an ever-increasing number of applications, explainability has emerged as a crucial desideratum for these models. In many real-world ta…
Fair Bayesian Optimization
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar +3
Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML model…
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
Till Speicher, Hoda Heidari, Nina Grgic-Hlaca +4
Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on defining conditions for fairness, but does not define satisfactory…
From Parity to Preference-based Notions of Fairness in Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +2
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.…
On Fairness, Diversity and Randomness in Algorithmic Decision Making
Nina Grgić-Hlača, Muhammad Bilal Zafar, Krishna P. Gummadi +1
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the…
Characterizing Information Diets of Social Media Users
Juhi Kulshrestha, Muhammad Bilal Zafar, Lisette Espin-Noboa +2
With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of…
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media
Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias +4
Search systems in online social media sites are frequently used to find information about ongoing events and people. For topics with multiple competing perspectives, such as politi…
The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems
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. Traditional active and passive approaches to make…
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +1
Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical dec…
Fairness Constraints: Mechanisms for Fair Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +1
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of d…