Socially Responsible Machine Learning

Accepted Paper

• Detecting and Quantifying Malicious Activity with Simulation-based Inference

  Andrew Gambardella (University of Oxford)*; Bogdan State (scie.nz); Naeemullah Khan (Oxford); Kleovoulos Tsourides (Facebook); Philip Torr (University of Oxford); Atilim Gunes Baydin (University of Oxford)

• Flexible Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles

  Ana Lucic (University of Amsterdam)*; Harrie Oosterhuis (Radboud University); Hinda Haned (University of Amsterdam); Maarten de Rijke (University of Amsterdam & Ahold Delhaize)

• Should Altruistic Benchmarks be the Default in Machine Learning?

  Marius Hobbhahn (University of Tübingen)*

• Have the Cake and Eat It Too? Higher Accuracy and Less Expense when Using Multi-label ML APIs Online

  Lingjiao Chen (Stanford University)*; James Zou (Stanford University); Matei Zaharia (Stanford and Databricks)

• Diverse and Amortised Counterfactual Explanations for Uncertainty Estimates

  Dan Ley (University of Cambridge)*; Umang Bhatt (University of Cambridge); Adrian Weller (University of Cambridge)

• Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

  Keegan Harris (Carnegie Mellon University)*; Dung Daniel T Ngo (University of Minnesota); Logan Stapleton (University of Minnesota); Hoda Heidari (Carnegie Mellon University); Steven Wu (Carnegie Mellon University)

• Stateful Strategic Regression

  Keegan Harris (Carnegie Mellon University)*; Hoda Heidari (Carnegie Mellon University); Steven Wu (Carnegie Mellon University)

• Machine Learning API Shift Assessments: Change is Coming!

  Lingjiao Chen (Stanford University)*; James Zou (Stanford University); Matei Zaharia (Stanford and Databricks)

• Improving Adversarial Robustness in 3D Point Cloud Classification via Self-Supervisions

  Jiachen Sun (University of Michigan)*; yulong cao (University of Michigan, Ann Arbor); Christopher Choy (Nvidia); Zhiding Yu (NVIDIA); Chaowei Xiao (University of Michigan, Ann Arbor); Anima Anandkumar (NVIDIA/Caltech); Zhuoqing Morley Mao (University of Michigan)

• Fairness in Missing Data Imputation

  Yiliang Zhang (University of Pennsylvania)*; Qi Long (University of Pennsylvania)

• Towards a Unified Framework for Fair and Stable Graph Representation Learning

  Chirag Agarwal (Harvard University)*; Himabindu Lakkaraju (Harvard); Marinka Zitnik (Harvard University)

• Stateful Performative Gradient Descent

  Zachary Izzo (Stanford)*; James Zou (Microsoft); Lexing Ying (Stanford University)

• FERMI: Fair Empirical Risk Minimization Via Exponential Rényi Mutual Information

  Andrew Lowy (USC); Rakesh Pavan (NITK); Sina Baharlouei (UNIVERSITY OF SOUTHERN CALIFORNIA); Meisam Razaviyayn (USC); Ahmad Beirami (Facebook AI)*

• Robust Counterfactual Explanations for Privacy-Preserving SVM

  Rami Mochaourab (RISE Research Institutes of Sweden)*; Sugandh Sinha (RISE Research Institutes of Sweden); Stanley Greenstein (Stockholm University); Panagiotis Papapetrou (Stockholm University)

• Auditing AI models for Verified Deployment under Semantic Specifications

  Homanga Bharadhwaj (University of Toronto, Vector Institute)*; De-An Huang (NVIDIA); Animashree Anandkumar (Caltech); Animesh Garg (University of Toronto, Vector Institute, Nvidia)

• Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning

  Rakshit Naidu (Carnegie Mellon University)*; Harshita Diddee (Bharati Vidyapeeth's College of Engineering); Ajinkya K Mulay (Purdue University); Aleti Vardhan (Manipal Institute of Technology); Krithika Ramesh (Manipal Institute of Technology); Ahmed S Zamzam (The National Renewable Energy Laboratory)

• Delving into the Remote Adversarial Patch in Semantic Segmentation

  yulong cao (University of Michigan, Ann Arbor )*; Jiachen Sun (University of Michigan); Qi Alfred Chen (UC Irvine); Zhuoqing Morley Mao (University of Michigan)

• Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions

  Kailas Vodrahalli (Stanford University)*; Tobias Gerstenberg (Stanford University); James Zou (Stanford University)

• Towards Explainable and Fair Supervised Learning

  Aarshee Mishra (University of Massachusetts, Amherst); Nicholas A Perello (University of Massachusetts Amherst)*; Przemyslaw A. Grabowicz (University of Massachusetts Amherst)

• Are You Man Enough? Even Fair Algorithms Conform to Societal Norms [Appendix]

  Myra Cheng (California Institute of Technology)*; Maria De-Arteaga (University of Texas at Austin); Lester Mackey (Microsoft Research); Adam Tauman Kalai (Microsoft Research)

• Margin-distancing for safe model explanation

  Tom Yan (Carnegie Mellon University)*; Chicheng Zhang (University of Arizona)

• CrossWalk: Fairness-enhanced Node Representation Learning

  Ahmad Khajehnejad (Sharif University of Technology)*; Moein Khajehnejad (Monash University); Mahmoudreza Babaei (Max Planck Institute for Human Development); Krishna Gummadi (MPI-SWS); Adrian Weller (University of Cambridge); Baharan Mirzasoleiman (UCLA)

• An Empirical Investigation of Learning from Biased Toxicity Labels

  Neel Nanda (DeepMind)*; Jonathan Uesato (DeepMind); Sven Gowal (DeepMind)

• Statistical Guarantees for Fairness Aware Plug-In Algorithms

  Drona Khurana (American Express)*; Srinivasan Ravichandran (American Express); Sparsh Jain (American Express); Narayanan U Edakunni (American Express AI Labs)

• Adversarial Stacked Auto-Encoders for Fair Representation Learning

  Patrik Joslin Kenfack (Innopolis University)*; Adil Khan (Innopolis University); Rasheed Hussain (Innopolis University); S.M. Ahsan Kazmi (Innopolis University)