Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
Publications
Publications
  • Article
  • Advances in Neural Information Processing Systems (NeurIPS)

Reliable Post hoc Explanations: Modeling Uncertainty in Explainability

By: Dylan Slack, Sophie Hilgard, Sameer Singh and Himabindu Lakkaraju
  • Format:Print
ShareBar

Abstract

As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by state-of-the-art techniques are inconsistent, unstable, and provide very little insight into their correctness and reliability. In addition, these methods are also computationally inefficient, and require significant hyper-parameter tuning. In this paper, we address the aforementioned challenges by developing a novel Bayesian framework for generating local explanations along with their associated uncertainty. We instantiate this framework to obtain Bayesian versions of LIME and KernelSHAP which output credible intervals for the feature importances, capturing the associated uncertainty. The resulting explanations not only enable us to make concrete inferences about their quality (e.g., there is a 95% chance that the feature importance lies within the given range), but are also highly consistent and stable. We carry out a detailed theoretical analysis that leverages the aforementioned uncertainty to estimate how many perturbations to sample, and how to sample for faster convergence. This work makes the first attempt at addressing several critical issues with popular explanation methods in one shot, thereby generating consistent, stable, and reliable explanations with guarantees in a computationally efficient manner. Experimental evaluation with multiple real world datasets and user studies demonstrate the efficacy of the proposed framework.

Keywords

Black Box Explanations; Bayesian Modeling; Decision Making; Risk and Uncertainty; Information Technology

Citation

Slack, Dylan, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju. "Reliable Post hoc Explanations: Modeling Uncertainty in Explainability." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
  • Read Now

About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

More from the Authors

    • June 2023
    • Transactions on Machine Learning Research (TMLR)

    When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making

    By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
    • 2023
    • Proceedings of the International Conference on Learning Representations (ICLR)

    Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

    By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
    • April 2023
    • Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)

    On the Privacy Risks of Algorithmic Recourse

    By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
More from the Authors
  • When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
  • Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
  • On the Privacy Risks of Algorithmic Recourse By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
ǁ
Campus Map
Harvard Business School
Soldiers Field
Boston, MA 02163
→Map & Directions
→More Contact Information
  • Make a Gift
  • Site Map
  • Jobs
  • Harvard University
  • Trademarks
  • Policies
  • Accessibility
  • Digital Accessibility
Copyright © President & Fellows of Harvard College