On sampling the wisdom of crowds: random vs. expert sampling of the twitter stream. - Saptarshi Ghosh 001; Muhammad Bilal Zafar; Parantapa Bhattacharya; Naveen Kumar Sharma; Niloy Ganguly; P. Krishna Gummadi (CIKM, 2013)

SplitBuff: Improving the interaction of heterogeneous RTT flows on the Internet. - Shahida Jabeen; Muhammad Bilal Zafar; Ihsan Ayyub Qazi; Zartash Afzal Uzmi (ICC, 2013)

Deep Twitter diving: exploring topical groups in microblogs at scale. - Parantapa Bhattacharya; Saptarshi Ghosh 001; Juhi Kulshrestha; Mainack Mondal; Muhammad Bilal Zafar; Niloy Ganguly; Krishna P. Gummadi (CSCW, 2014)

Inferring user interests in the Twitter social network. - Parantapa Bhattacharya; Muhammad Bilal Zafar; Niloy Ganguly; Saptarshi Ghosh 001; Krishna P. Gummadi (RecSys, 2014)

Strength in Numbers: Robust Tamper Detection in Crowd Computations. - Bimal Viswanath; Muhammad Ahmad Bashir; Muhammad Bilal Zafar; Simon Bouget; Saikat Guha 002; Krishna P. Gummadi; Aniket Kate; Alan Mislove (COSN, 2015)

Characterizing Information Diets of Social Media Users. - Juhi Kulshrestha; Muhammad Bilal Zafar; Lisette Espin Noboa; Krishna P. Gummadi; Saptarshi Ghosh 001 (ICWSM, 2015)

Fairness Constraints: A Mechanism for Fair Classification. - Muhammad Bilal Zafar; Isabel Valera; Manuel Gomez-Rodriguez; Krishna P. Gummadi (CoRR, 2015)

Sampling Content from Online Social Networks: Comparing Random vs. Expert Sampling of the Twitter Stream. - Muhammad Bilal Zafar; Parantapa Bhattacharya; Niloy Ganguly; Krishna P. Gummadi; Saptarshi Ghosh 001 (ACM Trans. Web, 2015)

On the Wisdom of Experts vs. Crowds: Discovering Trustworthy Topical News in Microblogs. - Muhammad Bilal Zafar; Parantapa Bhattacharya; Niloy Ganguly; Saptarshi Ghosh 001; Krishna P. Gummadi (CSCW, 2016)

Message Impartiality in Social Media Discussions. - Muhammad Bilal Zafar; Krishna P. Gummadi; Cristian Danescu-Niculescu-Mizil (ICWSM, 2016)

The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems. - Miguel Ferreira; Muhammad Bilal Zafar; Krishna P. Gummadi (CoRR, 2016)

Listening to Whispers of Ripple: Linking Wallets and Deanonymizing Transactions in the Ripple Network. - Pedro Moreno-Sanchez; Muhammad Bilal Zafar; Aniket Kate (Proc. Priv. Enhancing Technol., 2016)

Fairness Constraints: Mechanisms for Fair Classification. - Muhammad Bilal Zafar; Isabel Valera; Manuel Gomez-Rodriguez; Krishna P. Gummadi (AISTATS, 2017)

Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media. - Juhi Kulshrestha; Motahhare Eslami; Johnnatan Messias; Muhammad Bilal Zafar; Saptarshi Ghosh 001; Krishna P. Gummadi; Karrie Karahalios (CSCW, 2017)

From Parity to Preference-based Notions of Fairness in Classification. - Muhammad Bilal Zafar; Isabel Valera; Manuel Gomez-Rodriguez; Krishna P. Gummadi; Adrian Weller (NIPS, 2017)

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment. - Muhammad Bilal Zafar; Isabel Valera; Manuel Gomez-Rodriguez; Krishna P. Gummadi (WWW, 2017)

On Fairness, Diversity and Randomness in Algorithmic Decision Making. - Nina Grgic-Hlaca; Muhammad Bilal Zafar; Krishna P. Gummadi; Adrian Weller (CoRR, 2017)

Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning. - Nina Grgic-Hlaca; Muhammad Bilal Zafar; Krishna P. Gummadi; Adrian Weller (AAAI, 2018)

A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices. - Till Speicher; Hoda Heidari; Nina Grgic-Hlaca; Krishna P. Gummadi; Adish Singla; Adrian Weller; Muhammad Bilal Zafar (KDD, 2018)

Loss-Aversively Fair Classification. - Junaid Ali 001; Muhammad Bilal Zafar; Adish Singla; Krishna P. Gummadi (AIES, 2019)

Search bias quantification: investigating political bias in social media and web search. - Juhi Kulshrestha; Motahhare Eslami; Johnnatan Messias; Muhammad Bilal Zafar; Saptarshi Ghosh 001; Krishna P. Gummadi; Karrie Karahalios (Inf. Retr. J., 2019)

Fairness Constraints: A Flexible Approach for Fair Classification. - Muhammad Bilal Zafar; Isabel Valera; Manuel Gomez-Rodriguez; Krishna P. Gummadi (J. Mach. Learn. Res., 2019)

Discrimination in Algorithmic Decision Making: From Principles to Measures and Mechanisms. - Muhammad Bilal Zafar (2019)

Unifying Model Explainability and Robustness via Machine-Checkable Concepts. - Vedant Nanda; Till Speicher; John P. Dickerson; Krishna P. Gummadi; Muhammad Bilal Zafar (CoRR, 2020)

On the Lack of Robust Interpretability of Neural Text Classifiers. - Muhammad Bilal Zafar; Michele Donini; Dylan Slack; Cédric Archambeau; Sanjiv Das; Krishnaram Kenthapadi (ACL/IJCNLP (Findings), 2021)

Fair Bayesian Optimization. - Valerio Perrone; Michele Donini; Muhammad Bilal Zafar; Robin Schmucker; Krishnaram Kenthapadi; Cédric Archambeau (AIES, 2021)

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud. - Michaela Hardt; Xiaoguang Chen; Xiaoyi Cheng; Michele Donini; Jason Gelman; Satish Gollaprolu; John He; Pedro Larroy; Xinyu Liu; Nick McCarthy; Ashish Rathi; Scott Rees; et al. (KDD, 2021)

Multi-objective Asynchronous Successive Halving. - Robin Schmucker; Michele Donini; Muhammad Bilal Zafar; David Salinas; Cédric Archambeau (CoRR, 2021)

DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement. - Umang Bhatt; Isabel Chien; Muhammad Bilal Zafar; Adrian Weller (CoRR, 2021)

More Than Words: Towards Better Quality Interpretations of Text Classifiers. - Muhammad Bilal Zafar; Philipp Schmidt 002; Michele Donini; Cédric Archambeau; Felix Biessmann; Sanjiv Ranjan Das; Krishnaram Kenthapadi (CoRR, 2021)

Pairwise Fairness for Ordinal Regression. - Matthäus Kleindessner; Samira Samadi; Muhammad Bilal Zafar; Krishnaram Kenthapadi; Chris Russell 001 (AISTATS, 2022)

Generating Distributional Adversarial Examples to Evade Statistical Detectors. - Yigitcan Kaya; Muhammad Bilal Zafar; Sergül Aydöre; Nathalie Rauschmayr; Krishnaram Kenthapadi (ICML, 2022)

Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models. - David Nigenda; Zohar Karnin; Muhammad Bilal Zafar; Raghu Ramesha; Alan Tan; Michele Donini; Krishnaram Kenthapadi (KDD, 2022)

Diverse Counterfactual Explanations for Anomaly Detection in Time Series. - Déborah Sulem; Michele Donini; Muhammad Bilal Zafar; Francois-Xavier Aubet; Jan Gasthaus; Tim Januschowski; Sanjiv Das; Krishnaram Kenthapadi; Cédric Archambeau (CoRR, 2022)

What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations. - Parantapa Bhattacharya; Saptarshi Ghosh 001; Muhammad Bilal Zafar; Soumya K. Ghosh 001; Niloy Ganguly (CoRR, 2022)

Efficient fair PCA for fair representation learning. - Matthäus Kleindessner; Michele Donini; Chris Russell 001; Muhammad Bilal Zafar (AISTATS, 2023)

Hands-on Tutorial: "Explanations in AI: Methods, Stakeholders and Pitfalls". - Mia C. Mayer; Muhammad Bilal Zafar; Luca Franceschi 001; Huzefa Rangwala (KDD, 2023)

Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. - Luca Franceschi 001; Cemre Zor; Muhammad Bilal Zafar; Gianluca Detommaso; Cédric Archambeau; Tamas Madl; Michele Donini; Matthias W. Seeger (TML4H, 2023)

Understanding Developer-Analyzer Interactions in Code Reviews. - Martin Schäf; Berk Çirisci; Linghui Luo; Muhammad Numair Mansur; Omer Tripp; Daniel Sanchez; Qiang Zhou; Muhammad Bilal Zafar (ASE, 2024)

On Early Detection of Hallucinations in Factual Question Answering. - Ben Snyder; Marius Moisescu; Muhammad Bilal Zafar (KDD, 2024)

Evaluating Large Language Models with fmeval. - Pola Schwöbel; Luca Franceschi 001; Muhammad Bilal Zafar; Keerthan Vasist; Aman Malhotra; Tomer Shenhar; Pinal Tailor; Pinar Yilmaz; Michael Diamond; Michele Donini (CoRR, 2024)

The Impact of Inference Acceleration Strategies on Bias of LLMs. - Elisabeth Kirsten; Ivan Habernal; Vedant Nanda; Muhammad Bilal Zafar (CoRR, 2024)

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs). - Anna Neumann; Elisabeth Kirsten; Muhammad Bilal Zafar; Jatinder Singh (FAccT, 2025)

The Impact of Inference Acceleration on Bias of LLMs. - Elisabeth Kirsten; Ivan Habernal; Vedant Nanda; Muhammad Bilal Zafar (NAACL (Long Papers), 2025)

Can LLMs Explain Themselves Counterfactually? - Zahra Dehghanighobadi; Asja Fischer; Muhammad Bilal Zafar (CoRR, 2025)

When Should We Orchestrate Multiple Agents? - Umang Bhatt; Sanyam Kapoor; Mihir Upadhyay; Ilia Sucholutsky; Francesco Quinzan; Katherine M. Collins; Adrian Weller; Andrew Gordon Wilson; Muhammad Bilal Zafar (CoRR, 2025)

FinAI-BERT: A Transformer-Based Model for Sentence-Level Detection of AI Disclosures in Financial Reports. - Muhammad Bilal Zafar (CoRR, 2025)

Rote Learning Considered Useful: Generalizing over Memorized Data in LLMs. - Qinyuan Wu; Soumi Das; Mahsa Amani; Bishwamittra Ghosh; Mohammad Aflah Khan; Krishna P. Gummadi; Muhammad Bilal Zafar (CoRR, 2025)