Muhammad Bilal Zafar

Orcid: 0000-0001-8347-7813

  • Bosch Center for Artificial Intelligence, Renningen, Germany
  • Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken, Germany
  • Saarland University, Saarbrücken, Germany (PhD 2019)

According to our database1, Muhammad Bilal Zafar authored at least 39 papers between 2013 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


Online presence:



On Early Detection of Hallucinations in Factual Question Answering.
CoRR, 2023

Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography.
Proceedings of the Trustworthy Machine Learning for Healthcare, 2023

Hands-on Tutorial: "Explanations in AI: Methods, Stakeholders and Pitfalls".
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Efficient fair PCA for fair representation learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations.
CoRR, 2022

Diverse Counterfactual Explanations for Anomaly Detection in Time Series.
CoRR, 2022

Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Generating Distributional Adversarial Examples to Evade Statistical Detectors.
Proceedings of the International Conference on Machine Learning, 2022

Pairwise Fairness for Ordinal Regression.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

More Than Words: Towards Better Quality Interpretations of Text Classifiers.
CoRR, 2021

DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement.
CoRR, 2021

Multi-objective Asynchronous Successive Halving.
CoRR, 2021

Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Fair Bayesian Optimization.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

On the Lack of Robust Interpretability of Neural Text Classifiers.
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

Unifying Model Explainability and Robustness via Machine-Checkable Concepts.
CoRR, 2020

Discrimination in Algorithmic Decision Making: From Principles to Measures and Mechanisms.
PhD thesis, 2019

Fairness Constraints: A Flexible Approach for Fair Classification.
J. Mach. Learn. Res., 2019

Search bias quantification: investigating political bias in social media and web search.
Inf. Retr. J., 2019

Loss-Aversively Fair Classification.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

On Fairness, Diversity and Randomness in Algorithmic Decision Making.
CoRR, 2017

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment.
Proceedings of the 26th International Conference on World Wide Web, 2017

From Parity to Preference-based Notions of Fairness in Classification.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media.
Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 2017

Fairness Constraints: Mechanisms for Fair Classification.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Listening to Whispers of Ripple: Linking Wallets and Deanonymizing Transactions in the Ripple Network.
Proc. Priv. Enhancing Technol., 2016

The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems.
CoRR, 2016

Message Impartiality in Social Media Discussions.
Proceedings of the Tenth International Conference on Web and Social Media, 2016

On the Wisdom of Experts vs. Crowds: Discovering Trustworthy Topical News in Microblogs.
Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 2016

Sampling Content from Online Social Networks: Comparing Random vs. Expert Sampling of the Twitter Stream.
ACM Trans. Web, 2015

Fairness Constraints: A Mechanism for Fair Classification.
CoRR, 2015

Characterizing Information Diets of Social Media Users.
Proceedings of the Ninth International Conference on Web and Social Media, 2015

Strength in Numbers: Robust Tamper Detection in Crowd Computations.
Proceedings of the 2015 ACM on Conference on Online Social Networks, 2015

Inferring user interests in the Twitter social network.
Proceedings of the Eighth ACM Conference on Recommender Systems, 2014

Deep Twitter diving: exploring topical groups in microblogs at scale.
Proceedings of the Computer Supported Cooperative Work, 2014

SplitBuff: Improving the interaction of heterogeneous RTT flows on the Internet.
Proceedings of IEEE International Conference on Communications, 2013

On sampling the wisdom of crowds: random vs. expert sampling of the twitter stream.
Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013